A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

Medical Image Analysis

Volumn 13, Issue 5, 2009, Pages 757-770

  Murphy, K ^a   van Ginneken, B ^a   Schilham, A M R ^a   de Hoop, B J ^b   Gietema, H A ^b   Prokop, M ^b  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^b UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

Author keywords

Automatic detection; CT; kNN; Lung nodule; Nodule detection; Shape index

Indexed keywords

AUTOMATIC DETECTION; CT; KNN; LUNG NODULE; NODULE DETECTION; SHAPE INDEX;

BIOLOGICAL ORGANS; DATABASE SYSTEMS; MEDICAL IMAGING;

COMPUTERIZED TOMOGRAPHY;

ANTHROPOMETRY; ARTICLE; BIOMEDICAL TECHNOLOGY ASSESSMENT; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DATA ANALYSIS; DIAGNOSTIC ACCURACY; FALSE POSITIVE RESULT; IMAGE ANALYSIS; INFORMATION PROCESSING; K NEAREST NEIGHBOR; LUNG NODULE; LUNG VOLUME; NODULE SIZE; PRIORITY JOURNAL; RELIABILITY; SENSITIVITY ANALYSIS; THORAX RADIOGRAPHY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LUNG NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 68849111865     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2009.07.001     Document Type: Article

References (47)

1. American Cancer Society, 2008. Cancer Facts and Figures.
2. Aoki T., Nakata H., Watanabe H., Nakamura K., Kasai T., Hashimoto H., Yasumoto K., and Kido M. Evolution of peripheral lung adenocarcinomas: CT findings correlated with histology and tumor doubling time. American Journal of Roentgenology 174 3 (2000) 763-768
3. Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Sone S., and Doi K. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Academic Radiology 11 6 (2004) 617-629
4. Bae K.T., Kim J.S., Na Y.H., Kim K.G., and Kim J.H. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm - preliminary results. Radiology 236 (2005) 286-294
5. Bellotti R., De Carlo F., Gargano G., Tangaro S., Cascio D., Catanzariti E., Cerello P., Cheran S.C., Delogu P., De Mitri I., Fulcheri C., Grosso D., Retico A., Squarcia S., Tommasi E., and Golosio B. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Medical Physics 34 12 (2007) 4901-4910
6. Betke M., Hong H., and Ko J.P. Landmark detection in the chest and registration of lung surfaces with an application to nodule registration. Medical Image Analysis 7 (2003) 265-281
7. Brown M.S., Goldin J.G., Suh R.D., McNitt-Gray M.F., Sayre J.W., and Aberle D.R. Lung micronodules: automated method for detection at thin-section CT - initial experience. Radiology 226 (2003) 256-262
8. Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8 6 (1986) 679-698
9. Chang, S., Emoto, H., Metaxas, D.N., Axel, L., 2004. Pulmonary micronodule detection from 3D chest CT. In: Medical Image Computing and Computer-Assisted Intervention, vol. 3217, pp. 821-828.
10. Cover T., and Hart P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13 1 (1967) 21-27
11. Dehmeshki J., Ye X., Lin X., Valdivieso M., and Amin H. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Computerized Medical Imaging and Graphics 31 6 (2007) 408-417
12. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), John Wiley and Sons, New York
13. Enquobahrie A.A., Reeves A.P., Yankelevitz D.F., and Henschke C.I. Automated detection of small pulmonary nodules in whole lung CT scans. Academic Radiology 14 (2007) 579-593
14. Ge Z., Sahiner B., Chan H., Hadjiiski L.M., Cascade P.N., Bogot N., Kazerooni E.A., Wei J., and Zhou C. Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Medical Physics 32 8 (2005) 2443-2454
15. Gohagan J.K., Marcus P.M., Fagerstrom R.M., Pinsky P.F., Kramer B.S., Prorok P.C., Ascher S., Bailey W., Brewer B., Church T., Engelhard D., Ford M., Fouad M., Freedman M., Gelmann E., Gierada D., Hocking W., Inampudi S., Irons B., Johnson C.C., Jones A., Kucera G., Kvale P., Lappe K., Manor W., Moore A., Nath H., Neff S., Oken M., Moore A., Plunkett M., Price H., Reding D., Riley T., Schwartz M., Spizarny D., Yoffie R., Zylak C., and and the Lung Screening Study Research Group. Final results of the Lung Screening study, a randomized feasibility study of spiral CT versus chest X-ray screening for lung cancer. Lung Cancer 47 1 (2005) 9-15
16. Gurney J.W. Missed lung cancer at CT: imaging findings in nine patients. Radiology 199 (1996) 122-177
17. Henschke C.I., McCauley D.I., Yankelevitz D.F., Naidich D.P., McGuinness G., Miettinen O.S., Libby D., Pasmantier M., Koizumi J., Altorki N., and Smith J.P. Early lung cancer action project: a summary of the findings on baseline screening. The Oncologist 6 (2001) 147-152
18. Hu S., Hoffman E.A., and Reinhardt J.M. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Transactions on Medical Imaging 20 (2001) 490-498
19. New York Early Lung Cancer Action Project Investigators, 2007. CT Screening for lung cancer: diagnoses resulting from the New York Early Lung Cancer Action Project. Radiology 243(1), 239-249.
20. Jeong Y.J., Yi C.A., and Lee K.S. Solitary pulmonary nodules: detection, characterization, and guidance for further diagnostic workup and treatment. American Journal of Roentgenology 188 (2007) 57-68
21. Kakinuma R., Ohmatsu H., Kaneko M., Eguchi K., Naruke T., Nagai K., Nishiwaki Y., Suzuki A., and Moriyama N. Detection failures in spiral CT screening for lung cancer: analysis of CT findings. Radiology 212 (1999) 61-66
22. Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M., Kakinuma, R., Yamada, K., Mori, K., Nishiyama, H., Eguchi, K., Kaneko, M., Moriyama, N. (2004). Pulmonary nodule classification based on nodule retrieval from 3-D thoracic CT image database. In Medical image computing and computer-assisted intervention, vol. 3217, pp. 838-846.
23. Koenderink J.J. Solid Shape (1990), MIT Press, Cambridge, MA
24. Kostis W.J., Reeves A.P., Yankelevitz D.F., and Henschke C.I. Three-dimensional segmentation and growth rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging 22 10 (2003) 1259-1274
25. Li Q., Li F., and Doi K. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Academic Radiology 15 2 (2008) 165-175
26. Matsumoto S., Kundel H.L., Gee J.C., Gefter W.B., and Hatabu H. Pulmonary nodule detection in CT images with quantized convergence index filter. Medical Image Analysis 10 3 (2006) 343-352
27. McCulloch C.C., Kaucic R.A., Mendonça P.R.S., Walter D.J., and Avila R.S. Model-based detection of lung nodules in computed tomography exams. Academic Radiology 11 3 (2004) 258-266
28. Mendonça, P.R.S., Bhotika, R., Zhao, F., Miller, J.V., 2007. Lung nodule detection via Bayesian voxel labeling. In Information Processing in Medical Imaging, vol. 4584, pp. 134-146.
29. Metz C.E. ROC methodology in radiologic imaging. Investigative Radiology 21 9 (1986) 720-733
30. Mitra P., Murthy C.A., and Pal S.K. Density based multiscale data condensation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 6 (2002) 734-747
31. Murphy, K., Schilham, A.M.R., Gietema, H., Prokop, M., van Ginneken, B., 2007. Automated detection of pulmonary nodules from low-dose computed tomography scans using a two-stage classification system based on local image features. In Proceedings of the SPIE, vol. 6514, pp. 651410-1-651410-12.
32. Novello S., Fava C., Borasio P., Dogliotti L., Cortese G., Crida B., Selvaggi G., Lausi P., Brizzi M.P., Sperone P., Cardinale L., Ferraris F., Perotto F., Priola A., and Scagliotti G.V. Three-year findings of an early lung cancer detection feasibility study with low-dose spiral computed tomography in heavy smokers. Annals of Oncology 16 10 (2005) 1662-1666
33. Paik D.S., Beaulieu C.F., Rubin G.D., Acar B., Jeffrey Jr. R.B., Yee J., Dey J., and 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 23 6 (2004) 661-675
34. Pudil P., Novovicova J., and Kittler J. Floating search methods in feature selection. Pattern Recognition Letters 15 11 (1994) 1119-1125
35. Reeves A.P., Chan A.B., Yankelevitz D.F., Henschke C.I., Kressler B., and Kostis W.J. On measuring the change in size of pulmonary nodules. IEEE Transactions on Medical Imaging 25 4 (2006) 435-450
36. Retico A., Delogu P., Fantacci M.E., Gori I., and Preite Martinez A. Lung nodule detection in low-dose and thin-slice computed tomography. Computers in Biology and Medicine 38 4 (2008) 525-534
37. Rubin G.D., Lyo J.K., Paik D.S., Sherbondy A.J., Chow L.C., Leung A.N., Mindelzun R., Schraedley-Desmond P.K., Zinck S.E., Naidich D.P., and Napel S. Pulmonary nodules on multidetector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 234 (2005) 274-283
38. Sluimer I.C., Prokop M., and van Ginneken B. Towards automated segmentation of the pathological lung in CT. IEEE Transactions on Medical Imaging 24 8 (2005) 1025-1038
39. Swensen S.J., Jett J.R., Hartman T.E., Midthun D.E., Mandrekar S.J., Hillman S.L., Sykes A., Aughenbaugh G.L., Bungum A.O., and Allen K.L. CT screening for lung cancer: five-year prospective experience. Radiology 235 1 (2005) 259-265
40. Swensen S.J., Jett J.R., Sloan J.A., Midthun D.E., Hartman E.E., Sykes A., Aughenbaugh G.L., Zink F.E., Hillman S.L., Noetzel G.R., Marks R.S., Clayton A.C., and Pairolero P.C. Screening for lung cancer with low-dose spiral computed tomography. American Journal of Respiratory and Critical Care 165 (2002) 508-513
41. White C.S., Romney B.M., Mason A.C., Austin J.H., Miller B.H., and Protopapas Z. Primary carcinoma of the lung overlooked at CT: analysis of findings in 14 patients. Radiology 199 1 (1996) 109-115
42. Wiemker R., Rogalla P., Blaffert T., Sifri D., Hay O., Shah E., Truyen R., and Fleiter T. Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT. British Journal of Radiology 78 (2005) S46-S56 Spec No. 1
43. Xu D.M., Gietema H., de Koning H., Vernhout R., Nackaerts K., Prokop M., Weenink C., Lammers J., Groen H., Oudkerk M., and van Klaveren R. Nodule management protocol of the NELSON randomised lung cancer screening trial. Lung Cancer 54 2 (2006) 177-184
44. Ye, X., Lin, X., Beddoe, G., Dehmeshki, J., 2007. Efficient computer-aided detection of ground-glass opacity nodules in thoracic CT images. In: Conference Proceedings on IEEE Engineering in Medicine & Biology Society, vol. 1, pp. 4449-4452.
45. Zhang, X., McLennan, G., Hoffman, E.A., Sonka, M., 2005. Automated detection of small-size pulmonary nodules based on helical CT images. In: Information Processing in Medical Imaging, no. 3565, pp. 664-676.
46. Zhao B., Gamsu G., Ginsberg M.S., Jiang L., and Schwartz L.H. Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. Journal of Applied Clinical Medical Physics 4 3 (2003) 248-260
47. Zhou J., Chang S., Metaxas D.N., Zhao B., Schwartz L.H., and Ginsberg M.S. Automatic detection and segmentation of ground glass opacity nodules. Medical Image Computing and Computer-Assisted Intervention 9(Pt 1) (2006) 784-791