Computer-aided diagnosis systems for lung cancer: Challenges and methodologies

International Journal of Biomedical Imaging

Volumn 2013, Issue , 2013, Pages

  El Baz, Ayman ^a   Beache, Garth M ^b   Gimel'Farb, Georgy ^c   Suzuki, Kenji ^d   Okada, Kazunori ^e   Elnakib, Ahmed ^a   Soliman, Ahmed ^a   Abdollahi, Behnoush ^a  

^a University of Louisville   (United States)

^b University of Louisville School of Medicine   (United States)

^c UNIVERSITY OF AUCKLAND   (New Zealand)

^d UNIVERSITY OF CHICAGO   (United States)

^e SAN FRANCISCO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAD SYSTEM; COMPUTER-AIDED DIAGNOSIS SYSTEM; IMPLEMENTATION STEPS; LUNG CANCER; LUNG CANCER DIAGNOSIS; LUNG FIELDS; PROCESSING STEPS; RESEARCH STUDIES; STATE-OF-THE-ART TECHNIQUES; TRAINING AND TESTING;

COMPUTER AIDED DIAGNOSIS; DISEASES;

BIOLOGICAL ORGANS;

EID: 84874587318     PISSN: 16874188     EISSN: 16874196     Source Type: Journal    
DOI: 10.1155/2013/942353     Document Type: Review

References (364)

1. American Cancer Society,. Cancer facts and figures, 2012
2. El-Baz A., Suri J., Lung Imaging and Computer Aided Diagnosis 2011 Taylor & Francis
3. Hu S., Hoffman E. A., Reinhardt J. M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Transactions on Medical Imaging 2001 20 6 490 498 2-s2.0-0035354205 10.1109/42.929615 (Pubitemid 32657853)
4. Ukil S., Reinhardt J. M., Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Transactions on Medical Imaging 2009 28 2 202 214 2-s2.0-59449099817 10.1109/TMI.2008.929101
5. Van Rikxoort E. M., De Hoop B., Van De Vorst S., Prokop M., Van Ginneken B., Automatic segmentation of pulmonary segments from volumetric chest CT scans. IEEE Transactions on Medical Imaging 2009 28 4 621 630 2-s2.0-63849105518 10.1109/TMI.2008.2008968
6. Ross J. C., Estepar R. S. J., Daz A., Westin C. -F., Kikinis R., Silverman E. K., Washko G. R., Lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images. 5762 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '09) 2009 690 698
7. Otsu N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979 9 1 62 66 2-s2.0-0018306059 (Pubitemid 9413341)
8. Yim Y., Hong H., Shin Y. G., Hybrid lung segmentation in chest CT images for computer-aided diagnosis. 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005 June 2005 kor 378 383 2-s2.0-33745226208 10.1109/HEALTH.2005.1500486
9. Armato S. G., Giger M. L., Moran C. J., Blackburn J. T., Doi K., MacMahon H., Computerized detection of pulmonary nodules on CT scans. Radiographics 1999 19 5 1303 1311 2-s2.0-0033190949
10. Armato S. G. III, Sensakovic W. F., Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis. Academic Radiology 2004 11 9 1011 1021 2-s2.0-4544277237 10.1016/j.acra.2004.06.005 (Pubitemid 39220811)
11. Pu J., Leader J. K., Zheng B., Knollmann F., Fuhrman C., Sciurba F. C., Gur D., A computational geometry approach to automated pulmonary fissure segmentation in CT examinations. IEEE Transactions on Medical Imaging 2009 28 5 710 719 2-s2.0-65549169642 10.1109/TMI.2008.2010441
12. Pu J., Roos J., Yi C. A., Napel S., Rubin G. D., Paik D. S., Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Computerized Medical Imaging and Graphics 2008 32 6 452 462 2-s2.0-54049155078 10.1016/j.compmedimag.2008.04.005
13. Gao Q., Wang S., Zhao D., Liu J., Accurate lung segmentation for X-ray CT images. 2 Proceedings of the 3rd International Conference on Natural Computation (ICNC '07) 2007 275 279
14. Wei Q., Hu Y., Gelfand G., MacGregor J. H., Segmentation of lung lobes in high-resolution isotropic CT images. IEEE Transactions on Biomedical Engineering 2009 56 5 1383 1393 2-s2.0-67649202784 10.1109/TBME.2009.2014074
15. Ye X., Lin X., Dehmeshki J., Slabaugh G., Beddoe G., Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Transactions on Biomedical Engineering 2009 56 7 1810 1820 2-s2.0-67650862815 10.1109/TBME.2009.2017027
16. Gonzalez R. C., Woods R. E., Digital Image Processing 2007 3rd Prentice Hall
17. Itai Y., Kim H., Ishikawa S., Katsuragawa S., Ishida T., Nakamura K., Yamamoto A., Automatic segmentation of lung areas based on SNAKES and extraction of abnormal areas. Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '05) November 2005 377 381 2-s2.0-33845891855 10.1109/ICTAI.2005.44
18. Silveira M., Nascimento J., Marques J., Automatic segmentation of the lungs using robust level sets. Proceedings of the 29th IEEE Annual International Conference of Medicine and Biology Society (EMBS '07) 2007 4414 4417
19. Annangi P., Thiruvenkadam S., Raja A., Xu H., Sun X., Mao L., Region based active contour method for x-ray lung segmentation using prior shape and lowlevel features. Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: from Nano to Macro (ISBI '10) April 2010 892 895 2-s2.0-77955195408 10.1109/ISBI.2010.5490130
20. Chen Y., Tagare H. D., Thiruvenkadam S., Huang F., Wilson D., Gopinath K. S., Briggs R. W., Geiser E. A., Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision 2002 50 3 315 328 2-s2.0-0036892488 10.1023/A:1020878408985 (Pubitemid 35377418)
21. Chan T. F., Vese L. A., Active contours without edges. IEEE Transactions on Image Processing 2001 10 2 266 277 2-s2.0-0035248865 10.1109/83.902291 (Pubitemid 32254596)
22. Shi Y., Qi F., Xue Z., Chen L., Ito K., Matsuo H., Shen D., Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Transactions on Medical Imaging 2008 27 4 481 494 2-s2.0-41649087417 10.1109/TMI.2007.908130 (Pubitemid 351479141)
23. van Ginneken B., Stegmann M. B., Loog M., Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 2006 10 1 19 40 2-s2.0-28744449143 10.1016/j.media.2005.02.002 (Pubitemid 41754725)
24. Tsai A., Yezzi A., Wells W., Tempany C., Tucker D., Fan A., Grimson W. E., Willsky A., A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 2003 22 2 137 154 2-s2.0-0038736725 10.1109/TMI.2002.808355
25. Hardie R. C., Rogers S. K., Wilson T., Rogers A., Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Medical Image Analysis 2008 12 3 240 258 2-s2.0-43549123731 10.1016/j.media.2007.10.004
26. Sun S., Bauer C., Beichel R., Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach. IEEE Transactions on Medical Imaging 2012 31 2 449 460
27. Li K., Wu X., Chen D., Sonka M., Optimal surface segmentation in volumetric images-A graph-theoretic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006 28 1 119 134 (Pubitemid 46395309)
28. Besbes A., Paragios N., Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors. Proceedings of the International Symposium on Biomedical Imaging (ISBI '11) 2011 989 995
29. Sluimer I. C., Niemeijer M., Van Ginneken B., Lung field segmentation from thin-slice CT scans in presence of severe pathology. Proceedings of the Progress in Biomedical Optics and Imaging-Medical Imaging February 2004 1447 1455 2-s2.0-5644295486 10.1117/12.535312
30. Sluimer I., Prokop M., Van Ginneken B., Toward automated segmentation of the pathological lung in CT. IEEE Transactions on Medical Imaging 2005 24 8 1025 1038 2-s2.0-23844550632 10.1109/TMI.2005.851757 (Pubitemid 41178502)
31. Sofka M., Wetzl J., Birkbeck N., Zhang J., Kohlberger T., Kaftana J., Declerck J., Zhou S. K., Multi-stage learning for robust lung segmentation in challenging CT volumes. Proceedings of the International Conference on Medical 27 Imaging Computing and Computer-Assisted Intervention (MICCAI '11) 2011 667 674
32. Kockelkorn T. T. J. P., Van Rikxoort E. M., Grutters J. C., Van Ginneken B., Interactive lung segmentation in CT scans with severe abnormalities. Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '10) April 2010 564 567 2-s2.0-77955220246 10.1109/ISBI.2010.5490285
33. Hua P., Song Q., Sonka M., Hoffman E. A., Reinhardt J. M., Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm. Proceedings of the International Symposium on Biomedical Imaging (ISBI '11) 2011 2072 2075
34. El-Baz A., Gimel'farb G., Falk R., El-Baz A., Suri J., A novel three-dimensional framework for automatic lung segmentation from low dose computed tompgraphy images. Lung Imaging and Computer Aided Diagnosis 2011 chapter 1 Taylor & Francis 1 15
35. El-Baz A., Gimel'farb G., Falk R., Abou El-Ghar M., Holland T., Shaffer T., A new stochastic framework for accurate lung segmentation. Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '08) September 2008 New York, NY, USA 322 330
36. El-Baz A., Gimel'farb G., Falk R., Holland T., Shaffer T., A framework for unsupervised segmentation of lung tissues from low dose computed tomography images. Proceedings of the British Machine Vision Conference September 2008 Leeds, UK University of Leeds 855 865
37. El-Baz A., Gimelfarb G., EM based approximation of empirical distributions with linear combinations of discrete Gaussians. 2 Proceedings of the IEEE International Conference Image Processing (ICIP '07) September 2007 San Antonio, Tex, USA 373 376
38. El-Baz A., Elnakib A., Khalifa F., Abou El-Ghar M., Falk R., Gimelfarb G., Precise segmentation of 3d magnetic resonance angiography. IEEE Transactions on Biomedical Engineering 2012 59 7 2019 2029
39. Abdollahi B., Soliman A., Civelek A. C., Li X. F., Gimel'farb G., El-Baz A., A novel 3D joint MGRF framework for precise lung segmentation. Proceedings of the MICCAI Workshop on Machine Learning in Medical Imaging October 2012 Nice, France 86 93
40. Abdollahi B., Soliman A., Civelek A. C., Li X., Gimel'farb G., El-Baz A., A novel gaussian scale space-based joint MGRF framework for precise lung segmentation. Proceedings of the IEEE International Conference Image Processing (ICIP '12) October 2012 Orlando, Fla, USA 2029 2032
41. Ali A., El-Baz A., Farag A., A novel framework for accurate lung segmentation using graph cuts. Proceedings of the International Symposium on Biomedical Imaging (ISBI '07) April 2007 Arlington, Va, USA 908 911
42. Campadelli P., Casiraghi E., Artioli D., A fully automated method for lung nodule detection from postero-anterior chest radiographs. IEEE Transactions on Medical Imaging 2006 25 12 1588 1603 2-s2.0-33845567879 10.1109/TMI.2006. 884198 (Pubitemid 44936708)
43. Mendonca A. M., da Silva J. A., Campilho A., Automatic delimitation of lung fields on chest radiographs. 2 Proceedings of the International Symposium on Biomedical Imaging (ISBI '04) 2004 1287 1290 (Pubitemid 40514180)
44. Korfiatis P., Skiadopoulos S., Sakellaropoulos P., Kalogeropoulou C., Costaridou L., Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. British Journal of Radiology 2007 80 960 996 1005 2-s2.0-38349000626 10.1259/bjr/20861881
45. Kittler J., Illingworth J., Minimum error thresholding. Pattern Recognition 1986 19 1 41 47 2-s2.0-0022523996
46. Alliance for Lung Cancer Advocacy, Support, and Education: Early Detection and Diagnostic Imaging 2011
47. Quekel L. G. B. A., Kessels A. G. H., Goei R., Van Engelshoven J. M. A., Miss rate of lung cancer on the chest radiograph in clinical practice. Chest 1999 115 3 720 724 2-s2.0-0033065450 10.1378/chest.115.3.720 (Pubitemid 29129951)
48. Li F., Sone S., Abe H., MacMahon H., Armato S. G., Doi K., Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology 2002 225 3 673 683 2-s2.0-0036892529 (Pubitemid 35387101)
49. Boiselle P. M., White C. S., New Techniques in Thoracic Imaging 2002 New York, NY, USA Dekker
50. Kaneko M., Eguchi K., Ohmatsu H., Kakinuma R., Naruke T., Suemasu K., Moriyama N., Peripheral lung cancer: screening and detection with low-dose spinal CT versus radiography. Radiology 1996 201 3 798 802 2-s2.0-0029805812
51. Miettinen O. S., Henschke C. I., CT screening for lung cancer: coping with nihilistic recommendations. Radiology 2001 221 3 592 596 2-s2.0-0035185824 (Pubitemid 33101745)
52. Henschke C. I., Naidich D. P., Yankelevitz D. F., McGuinness G., McCauley D. I., Smith J. P., Libby D., Pasmantier M., Vazquez M., Koizumi J., Flieder D., Altorki N., Miettinen O. S., Early lung cancer action project: initial finding on repeat screening. Cancer 2001 92 1 153 159 (Pubitemid 32623052)
53. Swensen S. J., Jett J. R., Hartman T. E., Midthun D. E., Sloan J. A., Sykes A. M., Aughenbaugh G. L., Clemens M. A., Lung cancer screening with CT: mayo clinic experience. Radiology 2003 226 3 756 761 2-s2.0-0037372749 10.1148/radiol.2263020036 (Pubitemid 36259152)
54. Rusinek H., Naidich D. P., McGuinness G., Leitman B. S., McCauley D. I., Krinsky G. A., Clayton K., Cohen H., Pulmonary nodule detection: low-dose versus conventional CT. Radiology 1998 209 1 243 249 2-s2.0-0031660284 (Pubitemid 28464673)
55. Garg K., Keith R. L., Byers T., Kelly K., Kerzner A. L., Lynch D. A., Miller Y. E., Randomized controlled trial with low-dose spiral CT for lung cancer screening: feasibility study and preliminary results. Radiology 2002 225 2 506 510 2-s2.0-0036828463 (Pubitemid 35239255)
56. Nawa T., Nakagawa T., Kusano S., Kawasaki Y., Sugawara Y., Nakata H., Lung cancer screening using low-dose spiral CT: results of baseline and 1-year follow-up studies. Chest 2002 122 1 15 20 2-s2.0-0036308385 10.1378/chest.122.1. 15 (Pubitemid 34754137)
57. Sone S., Li F., Yang Z. G., Honda T., Maruyama Y., Takashima S., Hasegawa M., Kawakami S., Kubo K., Haniuda M., Yamanda T., Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. The British Journal of Cancer 2001 84 1 25 32 2-s2.0-17744391059 10.1054/bjoc.2000.1531 (Pubitemid 32102499)
58. Lo S. C., Freedman M. T., Lin J. S., Mun S. K., Automatic lung nodule detection using profile matching and back-propagation neural network techniques. Journal of Digital Imaging 1993 6 1 48 54 2-s2.0-33744808513 10.1007/BF03168418
59. Mao F., Qian W., Gaviria J., Clarke L. P., Fragmentary window filtering for multiscale lung nodule detection: preliminary study. Academic Radiology 1998 5 4 306 311 2-s2.0-0031824038 10.1016/S1076-6332(98)80231-6 (Pubitemid 28347723)
60. Matsumoto T., Yoshimura H., Doi K., Giger M. L., Kano A., MacMahon H., Abe K., Montner S. M., Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules. Investigative Radiology 1992 27 8 587 597 2-s2.0-0026629684
61. Xu X. W., Katsuragawa S., Ashizawa K., MacMahon H., Doi K., Analysis of image features of histograms of edge gradient for false positive reduction in lung nodule detection in chest radiographs. 3338 Proceedings of the Medical Imaging: Image Processing February 1998 318 326 2-s2.0-60849118976 10.1117/12.310908
62. Enquobahrie A. A., Reeves A. P., Yankelevitz D. F., Henschke C. I., Automated detection of pulmonary nodules from whole lung helical CT scans: performance comparison for isolated and attached nodules. Progress in Biomedical Optics and Imaging-Medical Imaging: Imaging Processing February 2004 791 800 Proceedings of SPIE 2-s2.0-5644226200 10.1117/12.536096
63. Mekada Y., Kusanagi T., Hayase Y., Mori K., Hasegawa J. i., Toriwaki J. i., Mori M., Natori H., Detection of small nodules from 3D chest X-ray CT images based on shape features. 1256 Proceedings of the Computer Assisted Radiology and Surgery (CARS) 2003 971 976
64. Ko J. P., Betke M., Chest CT: automated nodule detection and assessment of change over time-preliminary experience. Radiology 2001 218 1 267 273 2-s2.0-0035134661 (Pubitemid 32151306)
65. Zhao B., Ginsberg M. S., Lefkowitz R. A., Jiang L., Cooper C., Schwartz L. H., Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans. Proceedings of the Progress in Biomedical Optics and Imaging-Medical Imaging 2004: Imaging Processing February 2004 818 823 2-s2.0-5644282714 10.1117/12.535558
66. Chang S., Emoto H., Metaxas D. N., Axe L., Pulmonary micronodule detection from 3D chest CT. 3217 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '04) 2004 821 828 (Pubitemid 40787301)
67. Takizawa H., Shigemoto K., Yamamoto S., Matsumoto T., Tateno Y., Iinuma T., Matsumoto M., A recognition method of lung nodule shadows in X-Ray CT images using 3D object models. International Journal of Image and Graphics 2003 3 4 533 545
68. Li Q., Doi K., New selective nodule enhancement filter and its application for significant improvement of nodule detection on computed tomography. Medical Imaging: Imaging Processing February 2004 1 9 Proceedings of SPIE 2-s2.0-5644232874 10.1117/12.535802
69. Paik D. S., Beaulieu C. F., Rubin G. D., Acar B., Jeffrey R. B., Yee J., Dey J., Napel S., Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Transactions on Medical Imaging 2004 23 6 661 675 2-s2.0-2942577813 10.1109/TMI.2004.826362
70. Mendonca P. R. S., Bhotika R., Sirohey S. A., Turner W. D., Miller J. V., Avila R. S., Model-based analysis of local shape for lesion detection in CT scans. 8 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '05) 2005 688 695 (Pubitemid 43828310)
71. Lee Y., Hara T., Fujita H., Itoh S., Ishigaki T., Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Transactions on Medical Imaging 2001 20 7 595 604 2-s2.0-0035384869 10.1109/42.932744 (Pubitemid 32697978)
72. Wiemker R., Rogalla P., Zwartkruis A., Blaffert T., Computer aided lung nodule detection on high resolution CT data. 4684 Medical Imaging: Image Processing February 2002 677 688 Proceedings of SPIE 2-s2.0-0036032283 10.1117/12.467210
73. Kostis W. J., Reeves A. P., Yankelevitz D. F., Henschke C. I., Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging 2003 22 10 1259 1274 2-s2.0-0141954913 10.1109/TMI.2003.817785
74. Awai K., Murao K., Ozawa A., Komi M., Hayakawa H., Hori S., Nishimura Y., Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. Radiology 2004 230 2 347 352 2-s2.0-1642580603 10.1148/radiol.2302030049 (Pubitemid 38133790)
75. Ezoe T., Takizawa H., Yamamoto S., Shimizu A., Matsumoto T., Tateno Y., Iinuma T., Matsumoto M., An automatic detection method of lung cancers including ground glass opacities from chest X-ray CT images. 4684 Medical Imaging: Image Processing February 2002 1672 1680 Proceedings of SPIE 2-s2.0-0036030550 10.1117/12.467137
76. Fetita C. I., Prteux F., Beigelman-Aubry C., Grenier P., 3D automated lung nodule segmentation in HRCT. 2878 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '03) 2003 626 634
77. Tanino M., Takizawa H., Yamamoto S., Matsumoto T., Tateno Y., Iinuma T., A detection method of ground glass opacities in chest X-ray CT images using automatic clustering techniques. 5032 Medical Imaging: Image Processing February 2003 1728 1737 Proceedings of SPIE 2-s2.0-0042922542 10.1117/12.480294
78. Gurcan M. N., Sahiner B., Petrick N., Chan H. P., Kazerooni E. A., Cascade P. N., Hadjiiski L., Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Medical Physics 2002 29 11 2552 2558 2-s2.0-0036851688 10.1118/1.1515762 (Pubitemid 35375454)
79. Kubo M., Kubota K., Yamada N., Kawata Y., Niki N., Eguchi K., Ohmatsu H., Kakinuma R., Kaneko M., Kusumoto M., Mori K., Nishiyama H., Moriyama N., A CAD system for lung cancer based on low dose single-slice CT image. 4684 Medical Imaging: Image Processing February 2002 1262 1269 Proceedings of SPIE 2-s2.0-18644380385 10.1117/12.467086
80. Yamada N., Kubo M., Kawata Y., Niki N., Eguchi K., Omatsu H., Kakinuma R., Kaneko M., Kusumoto M., Nishiyama H., Moriyama N., ROI extraction of chest CT images using adaptive opening filter. 5032 Medical Imaging: Image Processing February 2003 869 876 Proceedings of SPIE 2-s2.0-0042876901 10.1117/12.483540
81. Kanazawa K., Kawata Y., Niki N., Satoh H., Ohmatsu H., Kakinuma R., Kaneko M., Moriyama N., Eguchi K., Computer-aided diagnosis for pulmonary nodules based on helical CT images. Computerized Medical Imaging and Graphics 1998 22 2 157 167 2-s2.0-0032026477 10.1016/S0895-6111(98)00017-2 (Pubitemid 28400204)
82. Kawata Y., Niki N., Ohmatsu H., Kusumoto M., Kakinuma R., Mori K., Nishiyama H., Eguchi K., Kaneko M., Moriyama N., Computeraided diagnosis of pulmonary nodules using three-dimensional thoracic CT images. 2208 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '01) 2001 1393 1394 (Pubitemid 33352631)
83. Betke M., Ko J. P., Detection of pulmonary nodules on CT and volumetric assessment of change over time. Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '99) 1999 245 252
84. Lampeter W., ANDS-V1 computer detection of lung nodules. 0555 Medical Imaging: Image Processing 1985 253 261 Proceedings of SPIE
85. Oda T., Kubo M., Kawata Y., Niki N., Eguchi K., Ohmatsu H., Kakinuma R., Kaneko M., Kusumoto M., Moriyama N., Mori K., Nishiyama H., A detection algorithm of lung cancer candidate nodules on multi-slice CT images. 5370 Medical Imaging 2002: Image Processing February 2002 1354 1361 Proceedings of SPIE 2-s2.0-18644374192 10.1117/12.467099
86. Saita S., Oda T., Kubo M., Kawata Y., Niki N., Sasagawa M., Ohmatsu H., Kakinuma R., Kaneko M., Kusumoto M., Eguchi K., Nishiyama H., Mori K., Moriyama N., Nodule detection algorithm based on multi-slice CT images for lung cancer screening. Medical Imaging: Imaging Processing February 2004 1083 1090 Proceedings of SPIE 2-s2.0-5644233833 10.1117/12.534826
87. Brown M. S., McNitt-Gray M. F., Goldin J. G., Suh R. D., Sayre J. W., Aberle D. R., Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Transactions on Medical Imaging 2001 20 12 1242 1250 2-s2.0-0035544610 10.1109/42.974919 (Pubitemid 34125133)
88. Giger M. L., Ahn N., Doi K., MacMahon H., Metz C. E., Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections. Medical Physics 1990 17 5 861 865 2-s2.0-0025135250 10.1118/1.596478 (Pubitemid 20353765)
89. Lin J. S., Ligomenides P. A., Lure Y. M. F., Freedman M. T., Mun S. K., Application of neural networks for improvement of lung nodule detection in radiographic images. Proceedings of the Symposium for Computer Assisted Radiology (SCAR '92) 1992 108 115
90. Carreira M. J., Cabello D., Penedo M. G., Pard J. M., Computer aided lung nodule detection in chest radiography. Image Analysis Applications and Computer Graphics 1995 1024 331 338 (Pubitemid 126013355)
91. Duda R. O., Hart P. E., Stork D. G., Pattern Classification 2001 2nd New Jersey, NJ, USA Wiley Interscience
92. Fukunaga K., Introduction to Statistical Pattern Recognition 1990 2nd San Diego, Calif, USA Academic Press
93. Wei G. Q., Fan L., Qian J., Automatic detection of nodules attached to vessels in lung CT by volume projection analysis. 2488 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '02) 2002 746 752
94. Takizawa H., Yamamoto S., Matsumoto T., Tateno Y., Iinuma T., Matsumoto M., Recognition of lung nodules from X-ray CT images using 3D Markov random field models. 4684 Medical Imaging: Image Processing February 2002 716 725 Proceedings of SPIE 2-s2.0-0036032309 10.1117/12.467214
95. Rumelhart D. E., Hinton G. E., Williams R. J., Learning representations by back-propagating errors. Nature 1986 323 6088 533 536 2-s2.0-0022471098 (Pubitemid 16025374)
96. Rumelhart D. E., Hinton G. E., Williams R. J., Learning internal representations by error propagation. Parallel Distributed Processing 1986 1 318 362
97. Zhang L., Fang M., Naidich D. P., Novak C. L., Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies. Medical Imaging: Imaging Processing February 2004 1709 1719 Proceedings of SPIE 2-s2.0-5644291974 10.1117/12.536259
98. Vapnik V. N., The Nature of Statistical Learning Theory 1995 Berlin, Germany Springer
99. Vapnik V. N., The Nature of Statistical Theory 1998 New York, NY, USA Wiley
100. Suzuki K., Pixel-based machine-learning PML in medical imaging. International Journal of Biomedical Imaging 2012 2012 18 792079 10.1155/2012/792079
101. Suzuki K., Horiba I., Sugie N., Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003 25 12 1582 1596
102. Suzuki K., Horiba I., Sugie N., Nanki M., Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Transactions on Medical Imaging 2004 23 3 330 339 2-s2.0-1642411032 10.1109/TMI.2004.824238
103. Lo S. C. B., Lou S. L. A., Lin J. S., Freedman M. T., Chien M. V., Mun S. K., Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging 1995 14 4 711 718 2-s2.0-0029546663 10.1109/42.476112
104. Lo S. C. B., Chan H. P., Lin J. S., Li H., Freedman M. T., Mun S. K., Artificial convolution neural network for medical image pattern recognition. Neural Networks 1995 8 7-8 1201 1214 2-s2.0-0029569057 10.1016/0893-6080(95) 00061-5 (Pubitemid 26040664)
105. Lin J. S., Shih-Chung B., Hasegawa A., Freedman M. T., Mun S. K., Reduction of false positives in lung nodule detection using a two-level neural classification. IEEE Transactions on Medical Imaging 1996 15 2 206 217 2-s2.0-0030126415 (Pubitemid 126780313)
106. Lo S. C. B., Li H., Wang Y., Kinnard L., Freedman M. T., A multiple circular path convolution neural network system for detection of mammographic masses. IEEE Transactions on Medical Imaging 2002 21 2 150 158 2-s2.0-0036462557 10.1109/42.993133 (Pubitemid 34466428)
107. Sahiner B., Chan H. P., Petrick N., Wei D., Helvie M. A., Adler D. D., Goodsitt M. M., Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Transactions on Medical Imaging 1996 15 5 598 610 (Pubitemid 126780349)
108. Wei D., Nishikawa R. M., Doi K., Application of texture analysis and shift-invariant artificial neural network to microcalcification cluster detection. Radiology 1996 201 696 696
109. Zhang W., Doi K., Giger M. L., Nishikawa R. M., Schmidt R. A., An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Medical Physics 1996 23 4 595 601 2-s2.0-0029933429 10.1118/1.597891 (Pubitemid 26120161)
110. Zhang W., Doi K., Giger M. L., Wu Y., Nishikawa R. M., Schmidt R. A., Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Medical Physics 1994 21 4 517 524 2-s2.0-0028334995 10.1118/1.597177 (Pubitemid 24129929)
111. Suzuki K., Armato S. G., Li F., Sone S., Doi K., Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics 2003 30 7 1602 1617 2-s2.0-0038710369 10.1118/1.1580485 (Pubitemid 36885778)
112. Suzuki K., Yoshida H., Näppi J., Dachman A. H., Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Medical Physics 2006 33 10 3814 3824 2-s2.0-33749391616 10.1118/1.2349839 (Pubitemid 44505106)
113. Suzuki K., Abe H., MacMahon H., Doi K., Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Transactions on Medical Imaging 2006 25 4 406 416 2-s2.0-33645673568 10.1109/TMI.2006.871549
114. Suzuki K., A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Physics in Medicine and Biology 2009 54 18 S31 S45 2-s2.0-71049155885 10.1088/0031-9155/54/18/S03
115. Suzuki K., Shiraishi J., Abe H., MacMahon H., Doi K., False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Academic Radiology 2005 12 2 191 201 2-s2.0-13944267171 10.1016/j.acra.2004.11.017 (Pubitemid 40269219)
116. Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Sone S., Doi K., Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Academic Radiology 2004 11 6 617 629 2-s2.0-2542474244 10.1016/j.acra.2004.02.009 (Pubitemid 38685165)
117. Suzuki K., Li F., Sone S., Doi K., Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Transactions on Medical Imaging 2005 24 9 1138 1150 2-s2.0-25144514408 10.1109/TMI.2005.852048 (Pubitemid 41337154)
118. Suzuki K., Rockey D. C., Dachman A. H., CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of «missed» polyps in a multicenter clinical trial. Medical Physics 2010 37 1 12 21 2-s2.0-73649110599 10.1118/1.3263615
119. Suzuki K., Yoshida H., Näppi J., Armato S. G., Dachman A. H., Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Medical Physics 2008 35 2 694 703 2-s2.0-38849178080 10.1118/1.2829870 (Pubitemid 351190889)
120. Suzuki K., Zhang J., Xu J., Massive-training artificial neural network coupled with laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 2010 29 11 1907 1917 2-s2.0-78149266353 10.1109/TMI.2010.2053213
121. Xu J. W., Suzuki K., Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Medical Physics 2011 38 4 1888 1902 2-s2.0-79953671750 10.1118/1.3562898
122. Li F., Arimura H., Suzuki K., Shiraishi J., Li Q., Abe H., Engelmann R., Sone S., MacMahon H., Doi K., Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005 237 2 684 690 2-s2.0-27144509110 10.1148/radiol.2372041555 (Pubitemid 41507913)
123. Giger M. L., Bae K. T., MacMahon H., Computerized detection of pulmonary nodules in computed tomography images. Investigative Radiology 1994 29 4 459 465 2-s2.0-0028300771 10.1097/00004424-199404000-00013 (Pubitemid 24222183)
124. Armato S. G., Giger M. L., MacMahon H., Automated detection of lung nodules in CT scans: preliminary results. Medical Physics 2001 28 8 1552 1561 2-s2.0-0034845242 10.1118/1.1387272 (Pubitemid 32783200)
125. Farag A., El-Baz A., Gimelfarb G., Falk R., Hushek S., Automatic detection and recognition of lung abnormalities in helical CT images using deformable templates. 2 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '04) September 2004 Saint-Malo, France 856 864
126. Farag A. A., El-Baz A., Gimelfarb G., El-Ghar M. A., Eldiasty T., Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for cad system design. 8 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '05) October 2005 Palm Springs, Calif, USA 720 728
127. Ge Z., Sahiner B., Chan H. P., Hadjiiski L. M., Cascade P. N., Bogot N., Kazerooni E. A., Wei J., Zhou C., Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Medical Physics 2005 32 8 2443 2454 2-s2.0-23944435413 10.1118/1.1944667 (Pubitemid 41197181)
128. Matsumoto S., Kundel H. L., Gee J. C., Gefter W. B., Hatabu H., Pulmonary nodule detection in CT images with quantized convergence index filter. Medical Image Analysis 2006 10 3 343 352 2-s2.0-33645886615 10.1016/j.media.2005.07.001
129. Yuan R., Vos P. M., Cooperberg P. L., Computer-aided detection in screening CT for pulmonary nodules. American Journal of Roentgenology 2006 186 5 1280 1287 2-s2.0-33745558637 10.2214/AJR.04.1969 (Pubitemid 46115734)
130. Pu J., Zheng B., Leader J. K., Wang X. H., Gur D., An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. Medical Physics 2008 35 8 3453 3461 2-s2.0-48349098875 10.1118/1.2948349
131. Retico A., Delogu P., Fantacci M. E., Gori I., Preite Martinez A., Lung nodule detection in low-dose and thin-slice computed tomography. Computers in Biology and Medicine 2008 38 4 525 534 2-s2.0-40949105934 10.1016/j.compbiomed. 2008.02.001
132. Golosio B., Masala G. L., Piccioli A., Oliva P., Carpinelli M., Cataldo R., Cerello P., De Carlo F., Falaschi F., Fantacci M. E., Gargano G., Kasae P., Torsello M., A novel multithreshold method for nodule detection in lung CT. Medical Physics 2009 36 8 3607 3618 2-s2.0-68149163602 10.1118/1.3160107
133. Murphy K., van Ginneken B., Schilham A. M. R., de Hoop B. J., Gietema H. A., Prokop M., A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Medical Image Analysis 2009 13 5 757 770 2-s2.0-68849111865 10.1016/j.media. 2009.07.001
134. Messay T., Hardie R. C., Rogers S. K., A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Medical Image Analysis 2010 14 3 390 406 2-s2.0-77951647196 10.1016/j.media.2010.02.004
135. Tan M., Deklerck R., Jansen Nad B., Bister M., Cornelis J., A novel computer-aided lung nodule detection system for CT images. Medical Physics 2011 38 10 5630 5645
136. Riccardi A., Petkov T. S., Ferri G., Masotti M., Campanini R., Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Medical Physics 2011 38 4 1962 1971 2-s2.0-79953648703 10.1118/1.3560427
137. Kawata Y., Niki N., Ohmatsu H., Kakinuma R., Eguchi K., Kaneko M., Moriyama N., Quantitative surface characterization of pulmonary nodules based on thin-section CT images. IEEE Transactions on Nuclear Science 1998 45 4 2132 2138 2-s2.0-0032142226 (Pubitemid 128739257)
138. Kawata Y., Niki N., Ohmatsu H., Moriyama N., A deformable surface model based on boundary and region information for pulmonary nodule segmentation from 3-D thoracic CT images. IEICE Transactions on Information and Systems 2003 86 9 1921 1930 2-s2.0-1642329932
139. Caselles V., Kimmel R., Sapiro G., Sbert C., Minimal surfaces based object segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997 19 4 394 398 2-s2.0-0031122564 (Pubitemid 127762894)
140. Yankelevitz D. F., Reeves A. P., Kostis W. J., Zhao B., Henschke C. I., Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000 217 1 251 256 2-s2.0-0034538087 (Pubitemid 33097610)
141. Yankelevitz D. F., Gupta R., Zhao B., Henschke C. I., Small pulmonary nodules: evaluation with repeat CT-preliminary experience. Radiology 1999 212 2 561 566 2-s2.0-0032765011 (Pubitemid 29356581)
142. Zhao B., Yankelevitz D., Reeves A., Henschke C., Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. Medical Physics 1999 26 6 889 895 2-s2.0-0032981298 10.1118/1.598605 (Pubitemid 29284106)
143. Zhao B., Reeves A. P., Yankelevitz D. F., Henschke C. I., Three-dimensional multicriterion automatic segmentation of pulmonary nodules of helical computed tomography images. Optical Engineering 1999 38 8 1340 1347 2-s2.0-0032663031 10.1117/1.602176
144. Wiemker R., Zwartkruis A., Optimal thresholding for 3D segmentation of pulmonary nodules in high resolution CT. International Congress Series 2001 1230 1 653 658
145. Xu N., Ahuja N., Bansal R., Automated lung nodule segmentation using dynamic programming and EM based classification. 4684 Medical Imaging: Image Processing February 2002 666 676 Proceedings of SPIE 2-s2.0-0036028336 10.1117/12.467209
146. Ko J. P., Rusinek H., Jacobs E. L., Babb J. S., Betke M., McGuinness G., Naidich D. P., Small pulmonary nodules: volume measurement at chest CT-phantom study. Radiology 2003 228 3 864 870 2-s2.0-0042931348 10.1148/radiol.2283020059 (Pubitemid 37025500)
147. Kostis W. J., Yankelevitz D. F., Reeves A. P., Fluture S. C., Henschke C. I., Small pulmonary nodules, reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 2004 231 2 446 452 2-s2.0-2142812310 10.1148/radiol.2312030553 (Pubitemid 38552903)
148. Okada K., Comaniciu D., Dalal N., Krishnan A., A robust algorithm for characterizing anisotropic local structures. 1 Proceedings of the European Conference on Computer Vision 2004 549 561
149. Okada K., Comaniciu D., Krishnan A., Scale selection for anisotropic scale-space: application to volumetric tumor characterization. 1 Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04) July 2004 I594 I601 2-s2.0-5044223752
150. Okada K., Comaniciu D., Krishnan A., Robust 3D segmentation of pulmonary nodules in multislice CT images. 2 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '04) 2004 881 889 (Pubitemid 40787308)
151. Okada K., Comaniciu D., Krishnan A., Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT. IEEE Transactions on Medical Imaging 2005 24 2 409 423 2-s2.0-19044373465 10.1109/TMI.2004.843172
152. Kuhnigk J. M., Dicken V., Bornemann L., Wormanns D., Krass S., Peitgen H. O., Fast automated segmentation and reproducible volumetry of pulmonary metastases in CT-scans for therapy monitoring. 3217 Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '04) September 2004 933 941 2-s2.0-20344374405
153. Kuhnigk J. M., Dicken V., Bornemann L., Bakai A., Wormanns D., Krass S., Peitgen H. O., Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Transactions on Medical Imaging 2006 25 4 417 434 2-s2.0-33645697952 10.1109/TMI.2006.871547
154. Mullally W., Betke M., Wang J., Ko J. P., Segmentation of nodules on chest computed tomography for growth assessment. Medical Physics 2004 31 4 839 848 2-s2.0-1942484737 10.1118/1.1656593 (Pubitemid 38508865)
155. Shen H., Goebel B., Odry B., A new algorithm for local surface smoothing with application to chest wall nodule segmentation in lung CT data. 5370 Medical Imaging: Imaging Processing February 2004 1519 1526 2-s2.0-5644244948 10.1117/12.535679
156. Zhang L., Zhang T., Novak C. L., Naidich D. P., Moses D. A., A computer-based method of segmenting ground glass nodules in pulmonary CT images: comparison to expert radiologists' interpretations. 5747 Proceedings of the Medical Imaging: image Processing February 2005 113 123 2-s2.0-23844556228 10.1117/12.595872
157. Okada K., Akdemir U., Krishnan A., Blob segmentation using joint space-intensity likelihood ratio test: application to 3D tumor segmentation. 2 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05) June 2005 437 444 2-s2.0-24644442879
158. Okada K., Ramesh V., Krishnan A., Singh M., Akdemir U., Robust pulmonary nodule segmentation in CT: improving performance for juxtapleural cases. 8 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '05) 2005 781 789
159. Okada K., Singh M., Ramesh V., Prior-constrained scale-space mean shift. Proceedings of the British Machine Vision Conference 2006 829 838
160. El-Baz A., Farag A., Gimel'farb G., Falk R., El-Ghar M. A., Eldiasty T., A framework for automatic segmentation of lung nodules from low dose chest CT scans. 3 Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06) August 2006 611 614 2-s2.0-34147095900 10.1109/ICPR.2006.66
161. Farag A., El-Baz A., Gimel'farb G., Falk R., El-Ghar M. A., Eldiasty T., Appearance models for robust segmentation of pulmonary nodules in 3D LDCT chest images. 9 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '06) 2006 662 670 (Pubitemid 44609162)
162. van Ginneken B., Supervised probabilistic segmentation of pulmonary nodules in CT scans. 9 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '06) 2006 912 919 (Pubitemid 44609308)
163. Way T. W., Hadjiiski L. M., Sahiner B., Chan H. P., Cascade P. N., Kazerooni E. A., Bogot N., Zhou C., Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Medical Physics 2006 33 7 2323 2337 2-s2.0-33745644734 10.1118/1.2207129 (Pubitemid 43967219)
164. Way T. W., Chan H. P., Goodsitt M. M., Sahiner B., Hadjiiski L. M., Zhou C., Chughtai A., Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study. Physics in Medicine and Biology 2008 53 5 1295 1312 2-s2.0-42649084308 10.1088/0031-9155/53/5/009 (Pubitemid 351594427)
165. Goodman L. R., Gulsun M., Washington L., Nagy P. G., Piacsek K. L., Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. American Journal of Roentgenology 2006 186 4 989 994 2-s2.0-33646913058 10.2214/AJR.04.1821 (Pubitemid 46100197)
166. Zhou J., Chang S., Metaxas D. N., Zhao B., Ginsberg M. S., Schwartz L. H., An automatic method for ground-glass opacity nodule detection and segmentation from CT studies. 1 Proceedings of the 29th IEEE Annual International Conference of Medicine and Biology Society (EMBS '06) 2006 3062 3065 (Pubitemid 46520555)
167. Zhou J., Chang S., Metaxas D. N., Zhao B., Schwartz L. H., Ginsberg M. S., Automatic detection and segmentation of ground-glass opacity nodules. 9 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '06) 2006 784 791 (Pubitemid 44609177)
168. Yoo Y., Shim H., Yun I. D., Lee K. W., Lee S. U., Segmentation of ground glass opacities by asymmetric multi-phase deformable model. 6144 Medical Imaging: Image Processing February 2006 2-s2.0-33745130739 10.1117/12.653053
169. Wang J., Engelmann R., Li Q., Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. Medical Physics 2007 34 12 4678 4689 2-s2.0-36649009312 10.1118/1.2799885 (Pubitemid 350197706)
170. Nie S. D., Li L. H., Chen Z. X., A CI feature-based pulmonary nodule segmentation using three-domain mean shift clustering. 223 Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR '07) November 2007 223 227 2-s2.0-45149097067 10.1109/ICWAPR.2007.4420699
171. Kobatake H., Hashimoto S., Convergence index filter for vector fields. IEEE Transactions on Image Processing 1999 8 8 1029 1038 2-s2.0-0032669269
172. Zheng Y., Steiner K., Bauer T., Yu J., Shen D., Kambhamettu C., Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework. Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07) October 2007 2-s2.0-50649105291 10.1109/ICCV.2007. 4409150
173. Zheng Y., Kambhamettu C., Bauer T., Steiner K., Accurate estimation of pulmonary nodule's growth rate in ct images with nonrigid registration and precise nodule detection and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '09) June 2009 101 108 2-s2.0-70449562419 10.1109/CVPR.2009.5204050
174. Browder W. A., Reeves A. P., Apananosovich T. V., Cham M. D., Yankelevitz D. F., Henschke C. I., Automated volumetric segmentation method for growth consistency of nonsolid pulmonary nodules in high-resolution. 6514 Medical Imaging: Computer-Aided Diagnosis February 2007 Proceedings of SPIE 2-s2.0-35248838430 10.1117/12.707775
175. Dehmeshki J., Amin H., Valdivieso M., Ye X., Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Transactions on Medical Imaging 2008 27 4 467 480 2-s2.0-41649099132 10.1109/TMI.2007.907555 (Pubitemid 351479140)
176. Diciotti S., Picozzi G., Falchini M., Mascalchi M., Villari N., Valli G., 3-D segmentation algorithm of small lung nodules in spiral CT images. IEEE Transactions on Information Technology in Biomedicine 2008 12 1 7 19 2-s2.0-39449138377 10.1109/TITB.2007.899504 (Pubitemid 351266638)
177. Kubota T., Jerebko A. K., Dewan M., Salganicoff M., Krishnan A., Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Medical Image Analysis 2011 15 1 133 154 2-s2.0-78449233034 10.1016/j.media.2010.08.005
178. Kubota T., Jerebko A., Salganicoff M., Dewan M., Krishnan A., Robust segmentation of pulmonary nodules of various densities: from ground-glass opacities to solid nodules. Proceedings of the International Workshop on Pulmonary Image Processing 2008 253 262
179. Zheng Y., Kambhamettu C., Bauer T., Steiner K., Estimation of ground-glass opacity measurement in CT lung images. 11 Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '08) 2008 238 245
180. Wang Q., Song E., Jin R., Han P., Wang X., Zhou Y., Zeng J., Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques. Academic Radiology 2009 16 6 678 688 2-s2.0-65549085126 10.1016/j.acra.2008.12.019
181. Tao Y., Lu L., Dewan M., Chen A. Y., Corso J., Xuan J., Salganicoff M., Krishnan A., Multi-level ground glass nodule detection and segmentation in CT lung images. 1 Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '09) 2009 715 723
182. Farag A. A., Abdelmunim H., Graham J., Farag A. A., Elshazly S., El-Mogy S., El-Mogy M., Falk R., Al-Jafary S., Mahdi H., Milam R., Variational approach for segmentation of lung nodules. Proceedings of the IEEE International Conference on Image Processing (ICIP '11) 2011 2157 2160
183. Zinoveva O., Zinovev D., Siena S. A., Raicu D. S., Furst J., Armato S. G., A texture-based probabilistic approach for lung nodule segmentation. 2 Proceedings of the International Conference on Image Analysis and Recognition 2011 21 30
184. Jirapatnakul Y. D., Mulman Y. D., Reeves A. P., Yankelevitz D. F., Henschke C. I., Segmentation of juxtapleural pulmonary nodules using a robust surface estimate. International Journal of Biomedical Imaging 2011 2011 14 632195 10.1155/2011/632195
185. Reeves A. P., Chan A. B., Yankelevitz D. F., Henschke C. I., Kressler B., Kostis W. J., On measuring the change in size of pulmonary nodules. IEEE Transactions on Medical Imaging 2006 25 4 435 450 2-s2.0-33645696746 10.1109/TMI.2006.871548
186. Diciotti S., Lombardo S., Falchini M., Picozzi G., Mascalchi M., Automated segmentation refinement of small lugn nodules in CT scans by local shape analysis. IEEE Transactions on Biomedical Engineering 2011 58 12 3418 3428
187. Kiffer J. D., Berlangieri S. U., Scott A. M., Quong G., Feigen M., Schumer W., Clarke C. P., Knight S. R., Daniel F. J., The contribution of 18F-fluoro-2-deoxy-glucose positron emission tomographic imaging to radiotherapy planning in lung cancer. Lung Cancer 1998 19 3 167 177 2-s2.0-0031596296 10.1016/S0169-5002(97)00086-X (Pubitemid 28238467)
188. Munley M. T., Marks L. B., Scarfone C., Sibley G. S., Patz E. F., Turkington T. G., Jaszczak R. J., Gilland D. R., Anscher M. S., Coleman R. E., Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer 1999 23 2 105 114 2-s2.0-0032975239 10.1016/S0169-5002(99)00005-7 (Pubitemid 29123880)
189. Nestle U., Walter K., Schmidt S., Licht N., Nieder C., Motaref B., Hellwig D., Niewald M., Ukena D., Kirsch C. M., Sybrecht G. W., Schnabel K., 18F-deoxyglucose positron emission tomography (FDG-PET) for the planning of radiotherapy in lung cancer: high impact in patients with atelectasis. International Journal of Radiation Oncology Biology Physics 1999 44 3 593 597 2-s2.0-0032586962 10.1016/S0360-3016(99)00061-9 (Pubitemid 29258434)
190. Mah K., Caldwell C. B., Ung Y. C., Danjoux C. E., Balogh J. M., Ganguli S. N., Ehrlich L. E., Tirona R., The impact of 18 FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. International Journal of Radiation Oncology Biology Physics 2002 52 2 339 350 2-s2.0-0036471788 10.1016/S0360-3016(01)01824-7 (Pubitemid 34172999)
191. Erdi Y. E., Rosenzweig K., Erdi A. K., Macapinlac H. A., Hu Y. C., Braban L. E., Humm J. L., Squire O. D., Chui C. S., Larson S. M., Yorke E. D., Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiotherapy and Oncology 2002 62 1 51 60 2-s2.0-0036161956 10.1016/S0167-8140(01)00470-4 (Pubitemid 34121949)
192. Bradley J., Thorstad W. L., Mutic S., Miller T. R., Dehdashti F., Siegel B. A., Bosch W., Bertrand R. J., Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. International Journal of Radiation Oncology Biology Physics 2004 59 1 78 86 2-s2.0-4444289151 10.1016/j.ijrobp. 2003.10.044 (Pubitemid 38501510)
193. Deniaud-Alexandre E., Touboul E., Lerouge D., Grahek D., Foulquier J. N., Petegnief Y., Grès B., El Balaa H., Keraudy K., Kerrou K., Montravers F., Milleron B., Lebeau B., Talbot J. N., Impact of computed tomography and 18F-deoxyglucose coincidence detection emission tomography image fusion for optimization of conformal radiotherapy in non-small-cell lung cancer. International Journal of Radiation Oncology Biology Physics 2005 63 5 1432 1441 2-s2.0-27744457325 10.1016/j.ijrobp.2005.05.016 (Pubitemid 41628039)
194. Van Der Wel A., Nijsten S., Hochstenbag M., Lamers R., Boersma L., Wanders R., Lutgens L., Zimny M., Bentzen S. M., Wouters B., Lambin P., De Ruysscher D., Increased therapeutic ratio by 18FDG-PET CT planning in patients with clinical CT stage N2-N3M0 non-small-cell lung cancer: a modeling study. International Journal of Radiation Oncology Biology Physics 2005 61 3 649 655 2-s2.0-13844294411 10.1016/j.ijrobp.2004.06.205 (Pubitemid 40247958)
195. Ashamalla H., Rafla S., Parikh K., Mokhtar B., Goswami G., Kambam S., Abdel-Dayem H., Guirguis A., Ross P., Evola A., The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer. International Journal of Radiation Oncology Biology Physics 2005 63 4 1016 1023 2-s2.0-27144526436 10.1016/j.ijrobp.2005.04.021 (Pubitemid 41505278)
196. Hatt M., Lamare F., Boussion N., Turzo A., Collet C., Salzenstein F., Roux C., Jarritt P., Carson K., Rest C. C. L., Visvikis D., Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Physics in Medicine and Biology 2007 52 12 3467 3491 2-s2.0-34249749945 10.1088/0031-9155/52/12/010 (Pubitemid 46830637)
197. Halt M., Le Rest C. C., Turzo A., Roux C., Visvikis D., A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Transactions on Medical Imaging 2009 28 6 881 893 2-s2.0-66249139913 10.1109/TMI.2008.2012036
198. Avazpour I., Roslan R. E., Bayat P., Saripan M. I., Nordin A. J., Abdullah R. S. A. R., Segmenting CT images of bronchogenic carcinoma with bone metastases using PET intensity markers approach. Radiology and Oncology 2009 43 3 180 186 2-s2.0-71049157271 10.2478/v10019-009-0023-y
199. Hatt M., Cheze le Rest C., Descourt P., Dekker A., De Ruysscher D., Oellers M., Lambin P., Pradier O., Visvikis D., Accurate Automatic Delineation of Heterogeneous Functional Volumes in Positron Emission Tomography for Oncology Applications. International Journal of Radiation Oncology Biology Physics 2010 77 1 301 308 2-s2.0-77950689496 10.1016/j.ijrobp.2009.08.018
200. Winer-Muram H. T., Jennings S. G., Tarver R. D., Aisen A. M., Tann M., Conces D. J., Meyer C. A., Volumetric growth rate of stage I lung cancer prior to treatment: serial CT scanning. Radiology 2002 223 3 798 805 2-s2.0-0036099035 (Pubitemid 34547600)
201. Borghesi A., Farina D., Maroldi R., Small pulmonary nodules: our preliminary experience in volumetric analysis of doubling times. Terarecon Inc, CA, Clinical Case Studies, http://www.terarecon.com/news/casestudy- PulmonaryNodules-BorghesiEtAl.pdf, 2007
202. Wormanns D., Kohl G., Klotz E., Marheine A., Beyer F., Heindel W., Diederich S., Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. European Radiology 2004 14 1 86 92 2-s2.0-0842345498 10.1007/s00330-003-2132-0 (Pubitemid 38181058)
203. Revel M. P., Lefort C., Bissery A., Bienvenu M., Aycard L., Chatellier G., Frija G., Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004 231 2 459 466 2-s2.0-2142700841 10.1148/radiol. 2312030241 (Pubitemid 38552905)
204. Goo J. M., Tongdee T., Tongdee R., Yeo K., Hildebolt C. F., Bae K. T., Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology 2005 235 3 850 856 2-s2.0-18844382442 10.1148/radiol.2353040737 (Pubitemid 40695880)
205. Jennings S. G., Winer-Muram H. T., Tann M., Ying J., Dowdeswell I., Distribution of stage I lung cancer growth rates determined with serial volumetric CT measurements. Radiology 2006 241 2 554 563 2-s2.0-33750380261 10.1148/radiol.2412051185 (Pubitemid 44632903)
206. Jirapatnakul A. C., Reeves A. P., Biancardi A. M., Yankelevitz D. F., Henschke C. I., Semi-automated measurement of pulmonary nodule growth without explicit segmentation. Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI '09) July 2009 855 858 2-s2.0-70449403418 10.1109/ISBI.2009.5193187
207. Marchianò A., Calabrò E., Civelli E., Di Tolla G., Frigerio L. F., Morosi C., Tafaro F., Ferri E., Sverzellati N., Camerini T., Mariani L., Lo Vullo S., Pastorino U., Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 2009 251 3 919 925 2-s2.0-67049145084 10.1148/radiol.2513081313
208. El-Baz A., Gimel'farb G., Falk R., Abo El-Ghar M., Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer. Pattern Recognition 2009 42 6 1041 1051 2-s2.0-59149087264 10.1016/j.patcog.2008.08.015
209. Trivedi M. M., Bezdek J. C., Low-level segmentation of aerial images with fuzzy clustering. IEEE Transactions on Systems, Man and Cybernetics 1986 16 4 589 598 2-s2.0-0022756144
210. Suzuki K., Doi K., How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Academic Radiology 2005 12 10 1333 1341 2-s2.0-25144467873 10.1016/j.acra.2005.06.017 (Pubitemid 41354259)
211. Chan H. P., Sahiner B., Wagner R. F., Petrick N., Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. Medical Physics 1999 26 12 2654 2668 2-s2.0-0033461791 10.1118/1.598805 (Pubitemid 30007711)
212. Sahiner B., Chan H. P., Hadjiiski L., Classifier performance prediction for computer-aided diagnosis using a limited dataset. Medical Physics 2008 35 4 1559 1570 2-s2.0-41449091519 10.1118/1.2868757 (Pubitemid 351457523)
213. El-Baz A., Farag A., Falk R., La Rocca R., Detection, visualization, and identification of lung abnormalities in chest spiral CT scans: phase I. Proceedings of the International Conference on Biomedical Engineering December 2002 Cairo, Egypt 38 42
214. El-Baz A., Farag A., Falk R., La Rocca R., Automatic identification of lung abnormalities in chest spiral CT scans. Proceedings of the International Conference Non Acoustics, Speech, and Signal Processing (ICASSP '03) April 2003 Hong kong, China 261 264
215. El-Baz A., Farag A., Falk R., La Rocca R., A unified approach for detection, visualization, and identification of lung abnormalities in chest spiral CT scans. Proceedings of the Computer Assisted Radiology and Surgery (CARS '03) June 2003 London, UK 998 1004
216. Farag A., El-Baz A., Gimel'farb G., Detection and recognition of lung nodules in spiral CT images using deformable templates and bayesian post-classification. 5 Proceedings of IEEE International Conference Image Processing (ICIP '04) October 2004 Singapore 2921 2924
217. Farag A., El-Baz A., Gimel'farb G., Detection and recognition of lung abnormalities using deformable templates. 3 Proceedings of the IARP International Conference Pattern Recognition (ICPR '04) August 2004 Cambridge, UK 738 741
218. El-Baz A., Farag A., Gimel'farb G., Falk R., Abo El-Ghar M., El-Baz A., Suri J., A novel level set-based computer-aided detection system for automatic detection of lung nodules in low dose chest computed tomography scans. Lung Imaging and Computer Aided Diagnosis 2011 chapter 10 Taylor & Francis 221 238
219. Armato S. G., McLennan G., McNitt-Gray M. F., Meyer C. R., Yankelevitz D., Aberle D. R., Henschke C. I., Hoffman E. A., Kazerooni E. A., MacMahon H., Reeves A. P., Croft B. Y., Clarke L. P., Lung image database consortium: developing a resource for the medical imaging research community. Radiology 2004 232 3 739 748 2-s2.0-4143056820 10.1148/radiol.2323032035 (Pubitemid 39100258)
220. Li Q., Li F., Suzuki K., Shiraishi J., Abe H., Engelmann R., Nie Y., MacMahon H., Doi K., Computer-aided diagnosis in thoracic CT. Seminars in Ultrasound, CT and MRI 2005 26 5 357 363 2-s2.0-26444499526 10.1053/j.sult.2005. 07.001 (Pubitemid 41436367)
221. Li Q., Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Computerized Medical Imaging and Graphics 2007 31 4-5 248 257 (Pubitemid 46604787)
222. Goldin J. G., Brown M. S., Petkovska I., Computer-aided diagnosis in lung nodule assessment. Journal of Thoracic Imaging 2008 23 2 97 104 2-s2.0-54049111428 10.1097/RTI.0b013e318173dd1f
223. Sluimer I., Schilham A., Prokop M., Van Ginneken B., Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging 2006 25 4 385 405 2-s2.0-33645683455 10.1109/TMI.2005.862753
224. Jaffe C. C., Measures of response: RECIST, WHO, and new alternatives. Journal of Clinical Oncology 2006 24 20 3245 3251 (Pubitemid 46622065)
225. Gavrielides M. A., Kinnard L. M., Myers K. J., Petrick N., Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 2009 251 1 26 37 2-s2.0-64949119251 10.1148/radiol.2511071897
226. Armato S. G., Li F., Giger M. L., MacMahon H., Sone S., Doi K., Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 2002 225 3 685 692 2-s2.0-0036891913 (Pubitemid 35387102)
227. Kawata Y., Niki N., Ohmatsu H., Kakinuma R., Eguchi K., Kaneko M., Moriyama N., Classification of pulmonary nodules in thin-section CT images based on shape characterization. 3 Proceedings of the International Conference on Image Processing (ICIP '97) October 1997 Part 2 (of 3) 528 530 2-s2.0-0031383571
228. Kubota T., Okada K., Estimating diameters of pulmonary nodules with competition-diffusion and robust ellipsoid fit. Proceedings of the ICCV Workshop on Computer Vision for Biomedical Image Applications 2005 324 334 (Pubitemid 43741538)
229. Bi J., Periaswamy S., Okada K., Kubota T., Fung G., Salganicoff M., Bharat Rao R., Computer aided detection via asymmetric cascade of sparse hyperplane classifiers. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06) August 2006 837 844 2-s2.0-33749578839
230. Henschke C. I., Yankelevitz D. F., Mateescu I., Brettle D. W., Rainey T. G., Weingard F. S., Neural networks for the analysis of small pulmonary nodules. Clinical Imaging 1997 21 6 390 399 2-s2.0-0030729353 10.1016/S0899-7071(97) 81731-7 (Pubitemid 27500876)
231. McNitt-Gray M. F., Hart E. M., Wyckoff N., Sayre J. W., Goldin J. G., Aberle D. R., A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Medical Physics 1999 26 6 880 888 2-s2.0-0032981748 10.1118/1.598603 (Pubitemid 29284105)
232. Kawata Y., Niki N., Ohmatsu H., Kusumoto M., Kakinuma R., Mori K., Nishiyama H., Eguchi K., Kaneko M., Moriyama N., Computerized analysis of 3-D pulmonary nodule images in surrounding and internal structure feature spaces. 2 Proceedings of IEEE International Conference on Image Processing (ICIP '01) October 2001 889 892 2-s2.0-0035163932
233. Kawata Y., Niki N., Ohmatsu H., Kusumoto M., Kakinuma R., Mori K., Nishiyama H., Eguchi K., Kaneko M., Moriyama N., Searching similar images for classification of pulmonary nodules in threedimensional CT images. Proceedings of the International Symposium on Biomedical Imaging (ISBI '02) 2002 189 193
234. Matsuki Y., Nakamura K., Watanabe H., Aoki T., Nakata H., Katsuragawa S., Doi K., Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. American Journal of Roentgenology 2002 178 3 657 663 2-s2.0-0036182280 (Pubitemid 34178147)
235. Lo S. C. B., Hsu L. Y., Freedman M. T., Lure Y. M. F., Zhao H., Classification of lung nodules in diagnostic CT: an approach based on 3-D vascular features, nodule density distributions, and shape features. 5032 Medical Imaging: Image Processing February 2003 183 189 Proceedings of SPIE 2-s2.0-0042376076 10.1117/12.481878
236. Aoyama M., Li Q., Katsuragawa S., Li F., Sone S., Doi K., Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Medical Physics 2003 30 3 387 394 2-s2.0-0037342845 10.1118/1.1543575 (Pubitemid 36337105)
237. Nakamura K., Yoshida M., Engelmann R., MacMahon H., Katsuragawa S., Ishida T., Ashizawa K., Doi K., Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology 2000 214 3 823 830 2-s2.0-0034009298 (Pubitemid 30115386)
238. Iwano S., Nakamura T., Kamioka Y., Ishigaki T., Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high-resolution CT. Computerized Medical Imaging and Graphics 2005 29 7 565 570 2-s2.0-27744522078 10.1016/j.compmedimag.2005.04.009 (Pubitemid 41606729)
239. Shah S. K., McNitt-Gray M. F., Rogers S. R., Goldin J. G., Suh R. D., Sayre J. W., Petkovska I., Kim H. J., Aberle D. R., Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. Academic Radiology 2005 12 10 1310 1319 2-s2.0-25144483323 10.1016/j.acra.2005.06.005 (Pubitemid 41354257)
240. Matsuoka S., Kurihara Y., Yagihashi K., Niimi H., Nakajima Y., Peripheral solitary pulmonary nodule: CT findings in patients with pulmonary emphysema. Radiology 2005 235 1 266 273 2-s2.0-15944372257 10.1148/radiol.2351040674 (Pubitemid 40432264)
241. Mori K., Niki N., Kondo T., Kamiyama Y., Kodama T., Kawada Y., Moriyama N., Development of a novel computer-aided diagnosis system for automatic discrimination of malignant from benign solitary pulmonary nodules on thin-section dynamic computed tomography. Journal of Computer Assisted Tomography 2005 29 2 215 222 2-s2.0-15744376050 10.1097/01.rct.0000155668.28514. 01 (Pubitemid 40411307)
242. Iwano S., Nakamura T., Kamioka Y., Ikeda M., Ishigaki T., Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT. Computerized Medical Imaging and Graphics 2008 32 5 416 422 2-s2.0-44149087054 10.1016/j.compmedimag.2008.04.001
243. Way T. W., Sahiner B., Chan H. P., Hadjiiski L., Cascade P. N., Chughtai A., Bogot N., Kazerooni E., Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Medical Physics 2009 36 7 3086 3098
244. Chen H., Xu Y., Ma Y., Ma B., Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation. Academic Radiology 2010 17 5 595 602
245. Lee M. C., Boroczky L., Sungur-Stasik K., Cann A. D., Borczuk A. C., Kawut S. M., Powell C. A., Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artificial Intelligence in Medicine 2010 50 1 43 53 2-s2.0-77955268440 10.1016/j.artmed.2010.04.011
246. El-Baz A., Gimel'farb G., Falk R., Abo El-Ghar M., Suri J., El-Baz A., Suri J., Appearance analysis for the early assessment of detected lung nodules. Lung Imaging and Computer Aided Diagnosis 2011 chapter 17 Taylor & Francis 395 404
247. El-Baz A., Nitzken M., Khalifa F., Elnakib A., Gimel'farb G., Falk R., Abo El-Ghar M., 3D shape analysis for early diagnosis of malignant lung nodules. Proceedings of the Information Processing in Medical Imaging (IPMI '11) July 2011 Irsee, Germany Monastery 772 783
248. Lee M. C., Wiemker R., Boroczky L., Sungur-Stasik K., Cann A. D., Borczuk A. C., Kawut S. M., Powell C. A., Impact of segmentation uncertainties on computer-aided diagnosis of pulmonary nodules. International Journal of Computer Assisted Radiology and Surgery 2008 3 6 551 558 2-s2.0-56749104298 10.1007/s11548-008-0257-y
249. Henschke C. I., McCauley D. I., Yankelevitz D. F., Naidich D. P., McGuinness G., Miettinen O. S., Libby D. M., Pasmantier M. W., Koizumi J., Altorki N. K., Smith J. P., Early lung cancer action project: overall design and findings from baseline screening. The Lancet 1999 354 9173 99 105 2-s2.0-0033542870 10.1016/S0140-6736(99)06093-6 (Pubitemid 29313143)
250. Henschke C. I., Yankelevitz D. F., Mirtcheva R., McGuinness G., McCauley D., Miettinen O. S., CT screening for lung cancer: frequency and significance of
251. Godoy M. C. B., Naidich D. P., Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology 2009 253 3 606 622 2-s2.0-73949120189 10.1148/radiol.2533090179
252. Min J. H., Lee H. Y., Lee K. S., Han J., Park K., Ahn M. J., Lee S. J., Stepwise evolution from a focal pure pulmonary ground-glass opacity nodule into an invasive lung adenocarcinoma: an observation for more than 10 years. Lung Cancer 2010 69 1 123 126 2-s2.0-77953357021 10.1016/j.lungcan.2010.04.022
253. Van Ginneken B., Ter Haar Romeny B. M., Viergever M. A., Computer-aided diagnosis in chest radiography: a survey. IEEE Transactions on Medical Imaging 2001 20 12 1228 1241 2-s2.0-0035544606 10.1109/42.974918 (Pubitemid 34129759)
254. Wei E., Yan J., Xu M., Zhang J. W., A novel segmentation algorithm for pulmonary nodule in chest radiograph. Proceedings of the IARP International Conference Pattern Recognition (ICPR '08) 2008 1 4
255. El-Baz A., Gimel'farb G., Falk R., Abo El-Ghar M., El-Baz A., Suri J., 3D MGRF-based appearance modeling for robust segmentation of pulmonary nodules in 3D LDCT chest images. Lung Imaging and Computer Aided Diagnosis 2011 chapter 3 Taylor & Francis 51 63
256. Jirapatnakul A. C., Fotin S. V., Reeves A. P., Biancardi A. M., Yankelevitz D. F., Henschke C. I., Automated nodule location and size estimation using a multi-scale Laplacian of Gaussian filtering approach. Proceedings of the 29th IEEE Annual International Conference of Medicine and Biology Society (EMBS '09) 2009 1028 1031
257. Diciotti S., Lombardo S., Coppini G., Grassi L., Falchini M., Mascalchi M., The LOG characteristic scale: a consistent measurement of lung nodule size in CT imaging. IEEE Transactions on Medical Imaging 2010 29 2 397 409 2-s2.0-76249122624 10.1109/TMI.2009.2032542
258. Kass M., Witkin A., Terzopoulos D., Snakes: active contour models. International Journal of Computer Vision 1988 1 4 321 331 2-s2.0-34250090755 10.1007/BF00133570
259. Caselles V., Kimmel R., Sapiro G., Geodesic Active Contours. International Journal of Computer Vision 1997 22 1 61 79 2-s2.0-0031071476 (Pubitemid 127507390)
260. Vese L. A., Chan T. F., A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision 2002 50 3 271 293 2-s2.0-0036891794 10.1023/A:1020874308076 (Pubitemid 35377416)
261. Amini A. A., Weymouth T. E., Jain R. C., Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990 12 9 855 867
262. Geiger D., Gupta A., Costa L. A., Vlontzos J., Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995 17 3 294 302 2-s2.0-0029276322 10.1109/34.368194
263. Comaniciu D., Meer P., Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002 24 5 603 619 2-s2.0-0036565814 10.1109/34.1000236 (Pubitemid 34550429)
264. Lin J., Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory 1991 37 1 145 151 2-s2.0-0025952277 10.1109/18.61115
265. Lindeberg T., Feature detection with automatic scale selection. International Journal of Computer Vision 1998 30 2 79 116 2-s2.0-0032209062 (Pubitemid 128517917)
266. Breiman L., Fiedman J., Stone C. J., Olshen R. A., Classification and Regression Trees 1984 Chapman and Hall/CRC
267. Boykov Y., Veksler O., Zabih R., Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 23 11 1222 1239 2-s2.0-0035509961 10.1109/34.969114 (Pubitemid 33137959)
268. Rueckert D., Sonoda L. I., Hayes C., Hill D. L. G., Leach M. O., Hawkes D. J., Non-rigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 1999 18 8 712 721 (Pubitemid 32262025)
269. Gietema H. A., Schaefer-Prokop C. M., Mali W. P. T. M., Groenewegen G., Prokop M., Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT-Influence of inspiration level, nodule size, and segmentation performance. Radiology 2007 245 3 889 894 2-s2.0-36249024875 10.1148/radiol.2452061054 (Pubitemid 350127361)
270. Rinaldi M. F., Bartalena T., Braccaioli L., Sverzellati N., Mattioli S., Rimondi E., Rossi G., Zompatori M., Battista G., Canini R., Three-dimensional analysis of pulmonary nodules: variability of semiautomated volume measurements between different versions of the same software. Radiologia Medica 2010 115 3 403 412 2-s2.0-77953233890 10.1007/s11547-010-0511-6
271. Hein P. A., Romano V. C., Rogalla P., Klessen C., Lembcke A., Bornemann L., Dicken V., Hamm B., Bauknecht H. C., Variability of semiautomated lung nodule volumetry on ultralow-dose ct: comparison with nodule volumetry on standard-dose CT. Journal of Digital Imaging 2010 23 1 8 17 2-s2.0-75749133314 10.1007/s10278-008-9157-5
272. Ashraf H., de Hoop B., Shaker S. B., Dirksen A., Bach K. S., Hansen H., Prokop M., Pedersen J. H., Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably. European Radiology 2010 20 8 1878 1885
273. Park C. M., Goo J. M., Lee H. J., Kim K. G., Kang M. J., Shin Y. H., Persistent pure ground-glass nodules in the lung: interscan variability of semiautomated volume and attenuation measurements. American Journal of Roentgenology 2010 195 6 W408 W414 2-s2.0-78649731914 10.2214/AJR.09.4157
274. de Hoop B., Gietema H., van Ginneken B., Zanen P., Groenewegen G., Prokop M., A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. European Radiology 2009 19 4 800 808
275. Van de Steene J., Linthout N., De Mey J., Vinh-Hung V., Claassens C., Noppen M., Bel A., Storme G., Definition of gross tumor volume in lung cancer: inter-observer variability. Radiotherapy and Oncology 2002 62 1 37 39 2-s2.0-0036164338 10.1016/S0167-8140(01)00453-4 (Pubitemid 34121948)
276. Paulino A. C., Johnstone P. A., FDG-PET in radiotherapy treatment planning: pandora's box? International Journal of Radiation Oncology Biology Physics 2004 59 1 4 5 (Pubitemid 38501500)
277. Giraud P., Grahek D., Montravers F., Carette M. F., Deniaud-Alexandre E., Julia F., Rosenwald J. C., Cosset J. M., Talbot J. N., Housset M., Touboul E., CT and 18F-deoxyglucose (FDG) image fusion for optimization of conformal radiotherapy of lung cancers. International Journal of Radiation Oncology Biology Physics 2001 49 5 1249 1257 2-s2.0-0035312195 10.1016/S0360-3016(00) 01579-0 (Pubitemid 32294305)
278. Nestle U., Kremp S., Schaefer-Schuler A., Sebastian-Welsch C., Hellwig D., Rübe C., Kirsch C. M., Comparison of different methods for delineation of18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. Journal of Nuclear Medicine 2005 46 8 1342 1348 2-s2.0-25444452894 (Pubitemid 43733485)
279. Black Q. C., Grills I. S., Kestin L. L., Wong C. Y. O., Wong J. W., Martinez A. A., Yan D., Defining a radiotherapy target with positron emission tomography. International Journal of Radiation Oncology Biology Physics 2004 60 4 1272 1282 2-s2.0-7444232598 10.1016/j.ijrobp.2004.06.254 (Pubitemid 39440814)
280. Davis J. B., Reiner B., Huser M., Burger C., Székely G., Ciernik I. F., Assessment of 18F PET signals for automatic target volume definition in radiotherapy treatment planning. Radiotherapy and Oncology 2006 80 1 43 50 2-s2.0-33746991225 10.1016/j.radonc.2006.07.006 (Pubitemid 44205313)
281. Daisne J. F., Sibomana M., Bol A., Doumont T., Lonneux M., Grégoire V., Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiotherapy and Oncology 2003 69 3 247 250 2-s2.0-0345303828 10.1016/S0167-8140(03)00270-6 (Pubitemid 37485998)
282. Van Dalen J. A., Hoffmann A. L., Dicken V., Vogel W. V., Wiering B., Ruers T. J., Karssemeijer N., Oyen W. J. G., A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nuclear Medicine Communications 2007 28 6 485 493 2-s2.0-34247574215 10.1097/MNM.0b013e328155d154 (Pubitemid 46684199)
283. Nehmeh S. A., El-Zeftawy H., Greco C., Schwartz J., Erdi Y. E., Kirov A., Schmidtlein C. R., Gyau A. B., Larson S. M., Humm J. L., An iterative technique to segment PET lesions using a Monte Carlo based mathematical model. Medical Physics 2009 36 10 4803 4809 2-s2.0-70349659686 10.1118/1.3222732
284. Caldwell C. B., Mah K., Skinner M., Danjoux C. E., Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. International Journal of Radiation Oncology Biology Physics 2003 55 5 1381 1393 2-s2.0-0037376967 10.1016/S0360-3016(02)04609-6 (Pubitemid 36338307)
285. Biehl K. J., Kong F. M., Dehdashti F., Jin J. Y., Mutic S., El Naqa I., Siegel B. A., Bradley J. D., 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? Journal of Nuclear Medicine 2006 47 11 1808 1812 2-s2.0-34447256267 (Pubitemid 47545003)
286. Pham D. L., Xu C., Prince J. L., Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2000 2 2000 315 337 2-s2.0-0034575445
287. Bezdek J. C., Hall L. O., Clark M. C., Goldgof D. B., Clarke L. P., Medical image analysis with fuzzy models. Statistical Methods in Medical Research 1997 6 3 191 214 2-s2.0-0031230813 (Pubitemid 127576066)
288. White C. J., Brady J. M., A semi-automatic approach to the delineation of tumour boundaries from PET data using level sets. Proceedings of the Society of Nuclear Medicine Annual Meeting 2005
289. Tylski P., Bonniaud G., Decencière E., Stawiaski J., Coulot J., Lefkopoulos D., Ricard M., 18F-FDG PET images segmentation using morphological watershed: a phantom study. 4 Proceedings of the IEEE Nuclear Science Symposium Conference Record November 2006 2063 2067 2-s2.0-38649084108 10.1109/NSSMIC.2006.354319
290. Zhu W., Jiang T., Automation segmentation of PET image for brain tumors. 4 Proceedings of the IEEE Nuclear Science Symposium Conference Record-Nuclear Science Symposium, Medical Imaging Conference October 2003 2627 2629 2-s2.0-11844273862
291. Montgomery D. W. G., Amira A., Zaidi H., Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Medical Physics 2007 34 2 722 736 2-s2.0-33846564887 10.1118/1.2432404 (Pubitemid 46185152)
292. Demirkaya O., Lesion segmentation in wholebody images of PET. 4 Proceedings of the IEEE Nuclear Science Symposium Conference Record-Nuclear Science Symposium, Medical Imaging Conference October 2003 2873 2876 2-s2.0-11844301302
293. Geets X., Lee J. A., Bol A., Lonneux M., Grégoire V., A gradientbased method for segmenting FDG-PET images: methodology and validation. European Journal of Nuclear Medicine and Molecular Imaging 2007 34 9 1427 1438 (Pubitemid 47312577)
294. Li H., Thorstad W. L., Biehl K. J., Laforest R., Su Y., Shoghi K. I., Donnelly E. D., Low D. A., Lu W., A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Medical Physics 2008 35 8 3711 3721 2-s2.0-48349101492 10.1118/1.2956713
295. Yu H., Caldwell C., Mah K., Mozeg D., Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Transactions on Medical Imaging 2009 28 3 374 383 2-s2.0-61449171165 10.1109/TMI.2008.2004425
296. Mohamed S. S., Youssef A. M., El-Saadany E. F., Salama M. M. A., Artificial life feature selection techniques for prostrate cancer diagnosis using TRUS images. Proceedings of the of 2nd International Conference on Image Analysis and Recognition 2005 903 913
297. Woods B. J., Clymer B. D., Kurc T., Heverhagen J. T., Stevens R., Orsdemir A., Bulan O., Knopp M. V., Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. Journal of Magnetic Resonance Imaging 2007 25 3 495 501 2-s2.0-33847610032 10.1002/jmri.20837 (Pubitemid 46355410)
298. Steenbakkers R. J. H. M., Duppen J. C., Fitton I., Deurloo K. E. I., Zijp L. J., Comans E. F. I., Uitterhoeve A. L. J., Rodrigus P. T. R., Kramer G. W. P., Bussink J., De Jaeger K., Belderbos J. S. A., Nowak P. J. C. M., Van Herk M., Rasch C. R. N., Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. International Journal of Radiation Oncology Biology Physics 2006 64 2 435 448 2-s2.0-30544446003 10.1016/j.ijrobp.2005.06.034 (Pubitemid 43083430)
299. Fox J. L., Rengan R., O'Meara W., Yorke E., Erdi Y., Nehmeh S., Leibel S. A., Rosenzweig K. E., Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non-small-cell lung cancer? International Journal of Radiation Oncology Biology Physics 2005 62 1 70 75 2-s2.0-17844388851 10.1016/j.ijrobp.2004.09.020 (Pubitemid 40591925)
300. De Ruysscher D., Wanders S., Van Haren E., Hochstenbag M., Geeraedts W., Utama I., Simons J., Dohmen J., Rhami A., Buell U., Thimister P., Snoep G., Boersma L., Verschueren T., Van Baardwijk A., Minken A., Bentzen S. M., Lambin P., Selective mediastinal node irradiation based on FDG-PET scan data in patients with non-small-cell lung cancer: a prospective clinical study. International Journal of Radiation Oncology Biology Physics 2005 62 4 988 994 2-s2.0-21244442951 10.1016/j.ijrobp.2004.12.019 (Pubitemid 40884408)
301. Reeves A. P., Biancardi A. M., Apanasovich T. V., Meyer C. R., MacMahon H., van Beek E. J. R., Kazerooni E. A., Yankelevitz D., McNitt-Gray M. F., McLennan G., Armato S. G., Henschke C. I., Aberle D. R., Croft B. Y., Clarke L. P., The lung image database consortium (LIDC). A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 2007 14 12 1475 1485 2-s2.0-36148953914 10.1016/j.acra.2007.09.005 (Pubitemid 350116645)
302. Bolte H., Jahnke T., Schäfer F. K. W., Wenke R., Hoffmann B., Freitag-Wolf S., Dicken V., Kuhnigk J. M., Lohmann J., Voß S., Knöß N., Heller M., Biederer J., Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels. European Journal of Radiology 2007 64 2 285 295 2-s2.0-35348912435 10.1016/j.ejrad.2007.02.031 (Pubitemid 47593063)
303. Das M., Ley-Zaporozhan J., Gietema H. A., Czech A., Mühlenbruch G., Mahnken A. H., Katoh M., Bakai A., Salganicoff M., Diederich S., Prokop M., Kauczor H. U., Günther R. W., Wildberger J. E., Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. European Radiology 2007 17 8 1979 1984 2-s2.0-34248632699 10.1007/s00330-006-0562-1 (Pubitemid 47097455)
304. Ravenel J. G., Leue W. M., Nietert P. J., Miller J. V., Taylor K. K., Silvestri G. A., Pulmonary nodule volume: effects of reconstruction parameters on automated measurements-a phantom study. Radiology 2008 247 2 400 408 2-s2.0-44449158674 10.1148/radiol.2472070868
305. El-Baz A., Sethu P., Gimel'farb G., Khalifa F., Elnakib A., Falk R., El-Ghar M. A., A new validation approach for the growth rate measurement using elastic phantoms generated by state-of-The-art microfluidics technology. Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10) September 2010 Hong Kong, China 4381 4384 2-s2.0-78651087366 10.1109/ICIP.2010.5652318
306. El-Baz A., Sethu P., Gimel'farb G., Khalifa F., Elnakib A., Falk R., Abo El-Ghar M., Suri J., El-Baz A., Suri J., Validation of a new imaged-based approach for the accurate estimating of the growth rate of detected lung nodules using real computed tomography images and elastic phantoms generated by state-of-the-art microfluidics technology. Lung Imaging and Computer Aided Diagnosis 2011 chapter 18 Taylor & Francis 405 420
307. El-Baz A., Sethu P., Gimel'farb G., Khalifa F., Elnakib A., Falk R., El-Ghar M. A., Elastic phantoms generated by microfluidics technology: validation of an imaged-based approach for accurate measurement of the growth rate of lung nodules. Biotechnology Journal 2011 6 2 195 203 2-s2.0-79551628217 10.1002/biot.201000105
308. Cornell University Vision and Image Analysis Group, Elcap public lung image database. http://www.via.cornell.edu/lungdb.html, 2003
309. McNitt-Gray M., Armato S. A. III, Meyer C., The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology 14 12 1464 1474
310. National Cancer Institute, Lidc datasets. http://imaging.cancer.gov/ programsandresources/informationsystems/lidc, 2005
311. Picozzi G., Paci E., Lopes Pegna A., Bartolucci M., Roselli G., De Francisci A., Gabbrielli S., Masi A., Perri F., Rottoli M. L., Carozzi F., Villari N., Mascalchi M., Screening of lung cancer with low dose spiral CT: results of a three year pilot study and design of the randomised controlled trial «Italung-CT» Radiologia Medica 2005 109 1-2 17 26 2-s2.0-16244392718 (Pubitemid 43942447)
312. Lopes Pegna A., Picozzi G., Mascalchi M., Maria Carozzi F., Carrozzi L., Comin C., Spinelli C., Falaschi F., Grazzini M., Innocenti F., Ronchi C., Paci E., Design, recruitment and baseline results of the ITALUNG trial for lung cancer screening with low-dose CT. Lung Cancer 2009 64 1 34 40 2-s2.0-61449129191 10.1016/j.lungcan.2008.07.003
313. Metz C. E., ROC methodology in radiologic imaging. Investigative Radiology 1986 21 9 720 733 (Pubitemid 16015664)
314. Hanley J. A., McNeil B. J., A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983 148 3 839 843 2-s2.0-0020524559 (Pubitemid 13033817)
315. Libby D. M., Smith J. P., Altorki N. K., Pasmantier M. W., Yankelevitz D., Henschke C. I., Managing the small pulmonary nodule discovered by CT. Chest 2004 125 4 1522 1529 2-s2.0-9344257538 10.1378/chest.125.4.1522 (Pubitemid 38501373)
316. Reeves A. P., Measurement methods for small pulmonary nodules. Radiology 2008 246 1 333 334 2-s2.0-37349067412 (Pubitemid 350305068)
317. Hasegawa M., Sone S., Takashima S., Li F., Yang Z. G., Maruyama Y., Watanabe T., Growth rate of small lung cancers detected on mass CT screening. British Journal of Radiology 2000 73 876 1252 1259 2-s2.0-0034491707 (Pubitemid 32123826)
318. Wilson D. O., Ryan A., Fuhrman C., Schuchert M., Shapiro S., Siegfried J. M., Weissfeld J., Doubling times and CT screendetected lung cancers in the pittsburgh lung screening study. American Journal of Respiratory and Critical Care Medicine 2012 185 1 85 89
319. Dewan N. A., Gupta N. C., Redepenning L. S., Phalen J. J., Frick M. P., Diagnostic efficacy of PET-FDG imaging in solitary pulmonary nodules: potential role in evaluation and management. Chest 1993 104 4 997 1002 2-s2.0-0027482637 (Pubitemid 23306039)
320. Gupta N. C., Maloof J., Gunel E., Probability of Malignancy in Solitary Pulmonary Nodules Using Fluorine-18-FDG and PET. Journal of Nuclear Medicine 1996 37 6 943 948 2-s2.0-0030162614 (Pubitemid 126447126)
321. Lowe V. J., Fletcher J. W., Gobar L., Lawson M., Kirchner P., Valk P., Karis J., Hubner K., Delbeke D., Heiberg E. V., Patz E. F., Coleman R. E., Prospective investigation of positron emission tomography in lung nodules. Journal of Clinical Oncology 1998 16 3 1075 1084 2-s2.0-0031935402 (Pubitemid 28108743)
322. Lee J., Aronchick J. M., Alavi A., Accuracy of F-18 fluorodeoxyglucose positron emission tomography for the evaluation of malignancy in patients presenting with new lung abnormalities: a retrospective review. Chest 2001 120 6 1791 1797 2-s2.0-0035684156 10.1378/chest.120.6.1791 (Pubitemid 34053435)
323. Herder G. J., Golding R. P., Hoekstra O. S., Comans E. F., Teule G. J., Postmus P. E., Smit E. F., The performance of 18F-fluorodeoxyglucose positron emission tomography in small solitary pulmonary nodules. European Journal of Nuclear Medicine and Molecular Imaging 2004 31 9 1231 1236 2-s2.0-4544239149 10.1007/s00259-004-1552-7 (Pubitemid 39222486)
324. Halley A., Hugentobler A., Icard P., Porret E., Sobrio F., Lerochais J. P., Bouvard G., Zalcman G., Agostini D., Efficiency of 18F-FDG and 99mTc-depreotide SPECT in the diagnosis of malignancy of solitary pulmonary nodules. European Journal of Nuclear Medicine and Molecular Imaging 2005 32 9 1026 1032 2-s2.0-25144437442 10.1007/s00259-005-1812-1 (Pubitemid 41339277)
325. Keidar Z., Haim N., Guralnik L., Wollner M., Bar-Shalom R., Ben-Nun A., Israel O., PET/CT using 18F-FDG in suspected lung cancer recurrence: diagnostic value and impact on patient management. Journal of Nuclear Medicine 2004 45 10 1640 1646 2-s2.0-13244296615 (Pubitemid 47618469)
326. Yi C. A., Kyung S. L., Kim B. T., Joon Y. C., Kwon O. J., Kim H., Young M. S., Myung J. C., Tissue characterization of solitary pulmonary nodule: comparative study between helical dynamic CT and integrated PET/CT. Journal of Nuclear Medicine 2006 47 3 443 450 2-s2.0-33645969925 (Pubitemid 46768487)
327. Nie Y., Li Q., Li F., Pu Y., Appelbaum D., Doi K., Integrating PET and CT information to improve diagnostic accuracy for lung nodules: a semiautomatic computer-aided method. Journal of Nuclear Medicine 2006 47 7 1075 1080 2-s2.0-33747105270 (Pubitemid 47544900)
328. Nakamoto Y., Senda M., Okada T., Sakamoto S., Saga T., Higashi T., Togashi K., Software-based fusion of PET and CT images for suspected recurrent lung cancer. Molecular Imaging and Biology 2008 10 3 147 153 2-s2.0-43249127752 10.1007/s11307-008-0131-x
329. Tao P., Griess F., Lvov Y., Mineyev M., Zhao B., Levin D., Kaufman L., Characterization of small nodules by automatic segmentation of X-ray computed tomography images. Journal of Computer Assisted Tomography 2004 28 3 372 377 2-s2.0-2342550126 10.1097/00004728-200405000-00012 (Pubitemid 38607998)
330. Petrou M., Quint L. E., Nan B., Baker L. H., Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology. American Journal of Roentgenology 2007 188 2 306 312 2-s2.0-33846527920 10.2214/AJR.05.1063 (Pubitemid 46167729)
331. Boll D. T., Gilkeson R. C., Fleiter T. R., Blackham K. A., Duerk J. L., Lewin J. S., Volumetric assessment of pulmonary nodules with ECG-gated MDCT. American Journal of Roentgenology 2004 183 5 1217 1223 2-s2.0-16544371693 (Pubitemid 44210578)
332. Honda O., Sumikawa H., Johkoh T., Tomiyama N., Mihara N., Inoue A., Tsubamoto M., Natsag J., Hamada S., Nakamura H., Computer-assisted lung nodule volumetry from multi-detector row CT: influence of image reconstruction parameters. European Journal of Radiology 2007 62 1 106 113 2-s2.0-33947122052 10.1016/j.ejrad.2006.11.017 (Pubitemid 46413624)
333. Sone S., Tsushima K., Yoshida K., Hamanaka K., Hanaoka T., Kondo R., Pulmonary Nodules. Preliminary Experience with Semiautomated Volumetric Evaluation by CT Stratum. Academic Radiology 2010 17 7 900 911 2-s2.0-77953124788 10.1016/j.acra.2010.03.003
334. Toshioka S., Kanazawa K., Niki N., Satoh H., Ohmatsu H., Eguchi K., Moriyama N., Computer aided diagnosis system for lung cancer based on helical CT images. 3034 Medical Imaging: Image Processing February 1997 975 984 Proceedings of SPIE 2-s2.0-0001054136 10.1117/12.274186
335. Kawata Y., Niki N., Ohmatsu H., Eguchi K., Moriyama N., Shape analysis of pulmonary nodules based on thin section CT images. 3034 Medical Imaging: Image Processing February 1997 964 974 Proceedings of SPIE 2-s2.0-57649227433 10.1117/12.274185
336. Kawata Y., Niki N., Ohmatsu H., Kusumoto M., Kakinuma R., Mori K., Nishiyama H., Eguchi K., Kaneko M., Moriyama N., Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images. 3979 Medical Imaging: Image Processing February 2000 2-s2.0-0033690008
337. Kawata Y., Niki N., Ohmatsu H., Kusumoto M., Kakinuma R., Mori K., Nishiyama H., Eguchi K., Kaneko M., Moriyama N., Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images. 4322 Medical Imaging: Image Processing February 2001 1890 1901 2-s2.0-17944373384 10.1117/12.431081
338. Hsieh J., Karau K., Theoretical prediction of lung nodule measurement accuracy under different acquisition and reconstruction conditions. 5369 Medical Imaging: Physiology, Function, and Structure from Medical Images February 2004 406 412 Proceedings of SPIE 2-s2.0-3042682023 10.1117/12.533542
339. El-Baz A., Gimel'farb G., Falk R., Abou El-Ghar M., Rainey S., Heredia D., Shaffer T., Toward early diagnosis of lung cancer. Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '09) September 2009 London, UK 682 689
340. El-Baz A., Gimel'farb G., Falk R., Heredia D., Abo El-Ghar M., A novel approach for accurate estimation of the growth rate of the detected lung nodules. Proceedings of the 1st International Workshop on Pulmonary Image Analysis September 2008 New York, NY, USA 33 42
341. El-Baz A., Gimel'farb G., Falk R., Abou El-Ghar M., A new approach for automatic analysis of 3D low dose CT images for accurate monitoring the detected lung nodules. Proceedings of IARP International Conference on Pattern Recognition (ICPR '08) December 2008 Tampa, Fla, USA 1 4
342. El-Baz A., Gimel'farb G., Falk R., Abou El-Ghar M., Refaie H., Promising results for early diagnosis of lung cancer. Proceedings of the International Symposium on Biomedical Imaging (ISBI '08) May 2008 Paris, France 1151 1154
343. El-Baz A., Gimel'farb G., Falk R., Abou El-Ghar M., A novel approach for automatic follow-up of detected lung nodules. 5 Proceedings of IEEE International Conference Image Processing (ICIP '07) September 2007 San Antonio, Tex, USA 501 504
344. El-Baz A., Gimelfarb G., Falk R., Abou El-Ghar M., A new CAD system for early diagnosis of detected lung nodules. 2 Proceedings of IEEE International Conference Image Processing (ICIP '07) September 2007 San Antonio, Tex, USA 461 464
345. El-Baz A., Yuksel S., Elshazly S., Farag A., Non-rigid registration techniques for automatic follow-up of lung nodules. Proceedings of the Computer Assisted Radiology and Surgery (CARS '05) June 2005 Berlin, Germany 1115 1120
346. El-Baz A., Khalifa F., Elnakib A., Nitkzen M., Soliman A., McClure P., Gimel'farb G., Abou El-Ghar M., A novel approach for global lung registration using 3D Markov Gibbs appearance model. Proceedings of the International Conference Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '12) October 2012 Nice, France 114 121
347. Furuya K., Murayama S., Soeda H., Murakami J., Ichinose Y., Yabuuchi H., Katsuda Y., Koga M., Masuda K., New classification of small pulmonary nodules by margin characteristics on high-resolution CT. Acta Radiologica 1999 40 5 496 504 2-s2.0-0033192759 (Pubitemid 129567261)
348. Kawata Y., Niki N., Ohmatsu H., Kakinuma R., Mori K., Eguchi K., Kaneko M., Moriyama N., Curvature based analysis of pulmonary nodules using thin-section CT images. 1 Proceedings of the IARP International Conference on Pattern Recognition (ICPR '98) 1998 361 363
349. Kido S., Kuriyama K., Higashiyama M., Kasugai T., Kuroda C., Fractal analysis of small peripheral pulmonary nodules in thin-section CT evaluation of the lung-nodule interfaces. Journal of Computer Assisted Tomography 2002 26 4 573 578 2-s2.0-0036657320 10.1097/00004728-200207000-00017
350. Shah S. K., McNitt-Gray M. F., Rogers S. R., Goldin J. G., Suh R. D., Sayre J. W., Petkovska I., Kim H. J., Aberle D. R., Computer-aided diagnosis of the solitary pulmonary nodule. Academic Radiology 2005 12 5 570 575 2-s2.0-18044398535 10.1016/j.acra.2005.01.018 (Pubitemid 40603683)
351. Gurney J. W., Swensen S. J., Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. Radiology 1995 196 3 823 829 2-s2.0-0029102693
352. El-Baz A., Gimel'farb G., Abou El-Ghar M., Falk R., Appearancebased diagnostic system for early assessment of malignant lung nodules. Proceedings of the IEEE International Conference on Image Processing (ICIP '12) September-October 2012 Orlando, Fla, USA 533 536
353. El-Baz A., Gimel'farb G., Falk R., Abo El-Ghar M., Appearance analysis for diagnosing malignant lung nodules. Proceedings of the International Symposium on Biomedical Imaging (ISBI '10) April 2010 Rotterdam, The Netherlands 193 196
354. El-Baz A., Gimel'farb G., Abo El-Ghar M., Falk R., Early assessment of malignant lung nodules. Proceedings of the of International Symposium on Biomedical Imaging (ISBI '12) May 2012 Barcelona, Spain 1463 1466
355. El-Baz A., Nitzken M., Gimel'farb G., Van Bogaert E., Falk R., Abo El-Ghar M., Suri J., El-Baz A., Suri J., Three-dimensional shape analysis using spherical harmonics for early assessment of detected lung nodules. Lung Imaging and Computer Aided Diagnosis 2011 chapter 19 Taylor & Francis 421 438
356. Kondapaneni M., Nitzken M., Bogaert E., Gimel'farb G., Falk R., Abou El-Ghar M., El-Baz A., A novel shape-based diagnostic approach for early diagnosis of lung nodules. Chest 2011 140 46, article 55A
357. El-Baz A., Nitzken M., Vanbogaert E., Gimel'farb G., Falk R., Abo El-Ghar M., A novel shape-based diagnostic approach for early diagnosis of lung nodules. Proceedings of the International Symposium on Biomedical Imaging (ISBI '11) March-April 2011 Chicago, Ill, USA 137 140
358. El-Baz A., Nitzken M., Khalifa F., Elnakib A., Gimel'farb G., Falk R., Abo El-Ghar M., 3D shape analysis for early diagnosis of malignant lung nodules. Proceedings of the International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI '11) September 2011 Toronto, Canada 587 594
359. Erasmus J. J., McAdams H. P., Connolly J. E., Solitary pulmonary nodules: part II. Evaluation of the indeterminate nodule. Radiographics 2000 20 1 59 66 2-s2.0-0033627915
360. Goo J. M., Im J. G., Do K. H., Yeo J. S., Seo J. B., Kim H. Y., Chung J. K., Pulmonary tuberculoma evaluated by means of FDG PET: findings in 10 cases. Radiology 2000 216 1 117 121 2-s2.0-0342647300 (Pubitemid 30434730)
361. Lardinois D., Weder W., Hany T. F., Kamel E. M., Korom S., Seifert B., Von Schulthess G. K., Steinert H. C., Staging of non-small-cell lung cancer with integrated positron-emission tomography and computed tomography. The New England Journal of Medicine 2003 348 25 2500 2507 2-s2.0-0037792992 10.1056/NEJMoa022136 (Pubitemid 36706537)
362. Erasmus J. J., McAdams H. P., Patz E. F., Coleman R. E., Ahuja V., Goodman P. C., Evaluation of primary pulmonary carcinoid tumors using FDG PET. American Journal of Roentgenology 1998 170 5 1369 1373 2-s2.0-0031898490 (Pubitemid 28210961)
363. Higashi K., Ueda Y., Seki H., Yuasa K., Oguchi M., Noguchi T., Taniguchi M., Tonami H., Okimura T., Yamamoto I., Fluorine-18-FDG PET imaging is negative in bronchioloalveolar lung carcinoma. Journal of Nuclear Medicine 1998 39 6 1016 1020 2-s2.0-0031863737 (Pubitemid 28270118)
364. Annema J. T., Hoekstra O. S., Smit E. F., Veseliç M., Versteegh M. I. M., Rabe K. F., Towards a minimally invasive staging strategy in NSCLC: analysis of PET positive mediastinal lesions by EUS-FNA. Lung Cancer 2004 44 1 53 60 2-s2.0-1542376208 10.1016/j.lungcan.2003.10.013 (Pubitemid 38324606)