Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

Medical Physics

Volumn 38, Issue 4, 2011, Pages 1844-1858

  Chen, Sheng ^a   Suzuki, Kenji ^a   MacMahon, Heber ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

chest radiography (CXR); computer aided diagnostic (CADe); lung cancer; support vector machine (SVM)

Indexed keywords

BIOLOGICAL ORGANS; CLUSTERING ALGORITHMS; DIAGNOSTIC RADIOGRAPHY; DISCRIMINANT ANALYSIS;

CHEST RADIOGRAPHY; COMPUTER AIDED DETECTION; COMPUTER AIDED DIAGNOSTICS; LEAVE-ONE-OUT CROSS VALIDATIONS; LINEAR DISCRIMINANT ANALYSIS; LUNG CANCER; NONLINEAR SUPPORT VECTOR MACHINES; SUPPORT VECTOR CLASSIFICATION;

SUPPORT VECTOR MACHINES;

EID: 79953663112     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.3561504     Document Type: Article

References (53)

1. American Cancer Society, American Cancer Society Complete Guide to Complementary & Alternative Cancer Therapies, 2nd ed. (American Cancer Society, Atlanta, GA, 2009).
2. J. V. Forrest and P. J. Friedman, "Radiologic errors in patients with lung cancer," West. J. Med. 134(6), 485-490 (1981).
3. C. I. Henschke et al., "Early lung cancer action project: Overall design and findings from baseline screening," Lancet 354(9173), 99-105 (1999).
4. G. P. Murphy et al., American Cancer Society Textbook of Clinical Oncology, 2nd ed. (The Society, Atlanta, GA, 1995).
5. G. Revesz, H. L. Kundel, and M. A. Graber, "The influence of structured noise on the detection of radiologic abnormalities," Invest. Radiol. 9(6), 479-486 (1974).
6. R. S. Fontana et al., "The Mayo Lung Project for early detection and localization of bronchogenic carcinoma: A status report," Chest 67(5), 511-522 (1975).
7. H. L. Kundel and G. Revesz, "Lesion conspicuity, structured noise, and film reader error," AJR, Am. J. Roentgenol. 126(6), 1233-1238 (1976).
8. J. H. Austin, B. M. Romney, and L. S. Goldsmith, "Missed bronchogenic carcinoma: Radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect," Radiology 182(1), 115-122 (1992).
9. P. K. Shah et al., "Missed non-small cell lung cancer: Radiographic findings of potentially resectable lesions evident only in retrospect," Radiology 226(1), 235-241 (2003).
10. M. L. Giger, K. Doi, and H. MacMahon, "Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields," Med. Phys. 15(2), 158-166 (1988).
11. B. Van Ginneken, B. M. ter Haar Romeny, and M. A. Viergever, "Computer-aided diagnosis in chest radiography: A survey," IEEE Trans. Med. Imaging 20(12), 1228-1241 (2001).
12. T. Kobayashi et al., "Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs," Radiology 199(3), 843-848 (1996).
13. H. MacMahon et al., "Computer-aided diagnosis of pulmonary nodules: Results of a large-scale observer test," Radiology 213(3), 723-726 (1999).
14. D. W. De Boo et al., "Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs," Eur. J. Radiol. 72(2), 218-225 (2009).
15. M. L. Giger et al., "Computerized detection of pulmonary nodules in digital chest images: Use of morphological filters in reducing falsepositive detections," Med. Phys. 17(5), 861-865 (1990).
16. S.C. Lo, M. T. Freedman, J. S. Lin, and S. K. Mun, "Automatic lung nodule detection using profile matching and back-propagation neural network techniques," J. Digit Imaging 6(1), 48-54 (1993).
17. S.-C. B. Lo et al., "Artificial convolution neural network techniques and applications to lung nodule detection," IEEE Trans. Med. Imaging 14(4), 711-718 (1995).
18. X. W. Xu et al., "Development of an improved CAD scheme for automated detection of lung nodules in digital chest images," Med. Phys. 24(9), 1395-1403 (1997).
19. H. Yoshida et al., "Computer-aided diagnosis scheme for detecting pulmonary nodules using wavelet transform," Proceedings of SPIE Medical Imaging: Image Processing, Vol. 2434, pp. 621-626, 1995 (unpublished).
20. N. F. Vittitoe, J. A. Baker, and C. E. Floyd, Jr., "Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules," Acad. Radiol. 4(2), 96-101 (1997).
21. M. J. Carreira et al., "Computer-aided diagnoses: Automatic detection of lung nodules," Med. Phys. 25(10), 1998-2006 (1998).
22. M. G. Penedo et al., "Computer-aided diagnosis: A neural-network-based approach to lung nodule detection," IEEE Trans. Med. Imaging 17(6), 872-880 (1998).
23. G. Coppini et al., "Neural networks for computer-aided diagnosis: Detection of lung nodules in chest radiograms," IEEE Trans. Inf. Technol. Biomed. 7(4), 344-357 (2003).
24. J. Shiraishi et al., "Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: Localized search method based on anatomical classification," Med. Phys. 33(7), 2642-2653 (2006).
25. A. M. Schilham, B. van Ginneken, and M. Loog, "A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database," Med. Image Anal. 10(2), 247-258 (2006).
26. P. Campadelli, E. Casiraghi, and D. Artioli, "A fully automated method for lung nodule detection from postero-anterior chest radiographs," IEEE Trans. Med. Imaging 25(12), 1588-1603 (2006).
27. R. C. Hardie et al., "Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs," Med. Image Anal. 12(3), 240-258 (2008).
28. F. Mao et al., "Fragmentary window filtering for multiscale lung nodule detection: Preliminary study," Acad. Radiol. 5(4), 306-311 (1998).
29. Q. Li, S. Katsuragawa, and K. Doi, "Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multipletemplate matching technique," Med. Phys. 28(10), 2070-2076 (2001).
30. J. Wei et al., "Optimal image feature set for detecting lung nodules on chest x-ray images," Computer Assisted Radiology and Surgery (Springer, Paris, 2002), pp. 706-711.
31. M. Freedman et al., "Computer-aided detection of lung cancer on chest radiographs: Effect of machine CAD true positive/false negative detections on radiologist's confidence level," Proceedings of SPIE Medical Imaging: Image Processing, Vol. 5372, pp. 185-191, 2004 (unpublished).
32. T. Matsumoto et al., "Potential usefulness of computerized nodule detection in screening programs for lung cancer," Invest. Radiol. 27(6), 471-475 (1992).
33. B. Keserci and H. Yoshida, "Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model," Med. Image Anal. 6(4), 431-447 (2002).
34. H. Yoshida, "Multiscale edge-guided wavelet snake model for delineation of pulmonary nodules in chest radiographs," J. Electron. Imaging 12(1), 69-80 (2003).
35. H. Yoshida, "Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules," IEEE Trans. Biomed. Eng. 51(5), 778-789 (2004).
36. K. Suzuki et al., "False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network," Acad. Radiol. 12(2), 191-201 (2005).
37. J. Shiraishi et al., "Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists' detection of pulmonary nodules," AJR, Am. J. Roentgenol. 174(1), 71-74 (2000).
38. J. A. Swets, "ROC analysis applied to the evaluation of medical imaging techniques," Invest. Radiol. 14(2), 109-121 (1979).
39. Y. Shi et al., "Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics," IEEE Trans. Med. Imaging 27(4), 481-494 (2008).
40. T. F. Cootes et al., "Use of active shape models for locating structures in medical images," Image Vis. Comput. 12(6), 355-365 (1994).
41. B. van Ginneken, M. B. Stegmann, and M. Loog, "Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database," Med. Image Anal. 10(1), 19-40 (2006).
42. D. Seghers et al., "Minimal shape and intensity cost path segmentation," IEEE Trans. Med. Imaging 26(8), 1115-1129 (2007).
43. B. van Ginneken et al., "Active shape model segmentation with optimal features," IEEE Trans. Med. Imaging 21(8), 924-933 (2002).
44. J. Serra, Image Analysis and Mathematical Morphology (Academic Press, Orlando, 1982), Vol. 1.
45. J. H. Austin et al., "Glossary of terms for CT of the lungs: Recommendations of the Nomenclature Committee of the Fleischner Society," Radiology 200(2), 327-331 (1996).
46. L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583-598 (1991).
47. K. Suzuki, I. Horiba, and N. Sugie, "Linear-time connected-component labeling based on sequential local operations," Comput. Vis. Image Underst. 89(1), 1-23 (2003).
48. D. Chakraborty, "Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data," Med. Phys. 16, 561-568 (1989).
49. B. Sahiner et al., "Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size," Med. Phys. 27(7), 1509-1522 (2000).
50. B. Sahiner, H. P. Chan, and L. Hadjiiski, "Classifier performance estimation under the constraint of a finite sample size: Resampling schemes applied to neural network classifiers," Neural Networks 21(2-3), 476-483 (2008).
51. B. Sahiner, H. P. Chan, and L. Hadjiiski, "Classifier performance prediction for computer-aided diagnosis using a limited dataset," Med. Phys. 35(4), 1559-1570 (2008).
52. R. M. Nishikawa et al., "Effect of case selection on the performance of computer-aided detection schemes," Med. Phys. 21(2), 265-269 (1994).
53. J. Shiraishi et al., "Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: Observer performance study," Acad. Radiol. 10(11), 1302-1311 (2003).