Medical Image Analysis
Volume 14, Issue 3 , Pages 390-406 , June 2010

A new computationally efficient CAD system for pulmonary nodule detection in CT imagery

  • Temesguen Messay

      Affiliations

    • Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0232, United States
    • Corresponding Author InformationCorresponding author. Tel.: +1 937 229 3611; fax: +1 937 229 4529.
  • ,
  • Russell C. Hardie

      Affiliations

    • Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0232, United States
  • ,
  • Steven K. Rogers

      Affiliations

    • Air Force Research Laboratory, AFRL/RY Wright Patterson AFB, OH 45433, United States

Received 7 November 2008 ,Revised 1 February 2010 ,Accepted 3 February 2010.

References 

  1. Anderson TW. R.A. Fisher and multivariate analysis. Statistical Science. 1996;11(1):20–34
  2. Armato S, Giger M, Moran C, Blackburn J, Doi K, MacMahon H. Computerized detection of pulmonary nodules on CT scans. RadioGraphics. 1999;19(5):1303–1311
  3. Armato S, Giger M, MacMahon H. Automated detection of lung nodules in CT scans: preliminary results. Medical Physics. 2001;28:1552–1561
  4. Armato S, McLennan G, McNitt-Gray M, Meyer C, Yankelevitz D, Aberle D, et al Lung image database consortium: developing a resource for the medical imaging research community. Radiology. 2004;232(3):739
  5. Armato S, McNitt-Gray M, Reeves A, Meyer C, McLennan G, Aberle D, et al The lung image database consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology. 2007;14(11):1409–1421
  6. Brown M, McNitt-Gray M, Mankovich N, Goldin J, Hiller J, Wilson L, et al. Method for segmenting chest CT image data using an anatomical model: preliminary results. IEEE Transactions on Medical Imaging. 1997;16(6):828–839
  7. Buhmann S, Herzog P, Liang J, Wolf M, Salganicoff M, Kirchhoff C, et al. Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism. Academic Radiology. 2007;14(6):651–658
  8. “Cancer Facts and Figs. 2009,” The American Cancer Society, 2009.
  9. Das M, Muhlenbruch G, Mahnken A, Flohr T, Gundel L, Stanzel S, et al. Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology. 2006;241(2):564
  10. Duda R, Hart P, Stork D. Pattern Classification. second ed.. Wiley Interscience; 2000;
  11. Ernst, R., Hardie, R., Gurcan, M., Oto, A., Rogers, S., Hoffmeister, J., 2004. Cad performance analysis for pulmonary nodule detection: comparison of thick- and thin-slice helical CT. Radiology Society of North America (RSNA).
  12. Fisher RA. The use of multiple measurements in taxonomic problems. Annals of Eugenics. 1936;7:179–188
  13. Fukunaga K. Introduction to Statistical Pattern Recognition. Academic Press, Inc.; 1990;
  14. Gonzalez R, Woods R. Digital Image Processing. Prentice Hall; 2007;
  15. Gori, I., Fantacci, M., Preite Martinez, A., Retico, A., 2007. An automated system for lung nodule detection in low-dose computed tomography. In: Giger, Maryellen, L., Karssemeijer, Nico (Eds.), Proceedings of the SPIE on Medical Imaging 2007: Computer-Aided Diagnosis, vol. 6514, p. 65143R.
  16. Gurung J, Maataoui A, Khan M, Wetter A, Harth M, Jacobi V, et al. Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. Rofo. 2006;178(1):71–77
  17. Hadjiiski L, Sahiner B, Chan H, Petrick N, Helvie M. Classification of malignant and benign masses based on hybrid ART2LDA approach. IEEE Transactions on Medical Imaging. 1999;18(12):1178–1187
  18. Hardie R, Rogers S, 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
  19. Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, et al. Early lung cancer action project: overall design and findings from baseline screening. Lancet. 1999;354:99–105
  20. Hoffman E, McLennan G. Assessment of the pulmonary structure–function relationship and clinical outcomes measures: quantitative volumetric ct of the lung. Academic Radiology. 1997;4(11):758–776
  21. Hu S, Hoffman E, Reinhardt J, et al. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Transactions on Medical Imaging. 2001;20(6):490–498
  22. Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, et al. Computer-aided diagnosis for pulmonary nodules based on helical CT images. Computerized Medical Imaging and Graphics. 1998;22(2):157–167
  23. Leader J, Zheng B, Rogers R, Sciurba F, Perez A, Chapman B, et al. Automated lung segmentation in X-ray computed tomography development and evaluation of a heuristic threshold-based scheme 1. Academic Radiology. 2003;10(11):1224–1236
  24. 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
  25. Martinez AM, Kak AC. PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;23(2):228–233
  26. Maxion, R., Roberts, R., 2004. Proper Use of ROC Curves in Intrusion/Anomaly Detection. University of Newcastle upon Tyne, School of Computing Science, UK. Technical Report Series: CS-TR-871, November 2004.
  27. McLachlan G. Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience; 2004;
  28. McNitt-Gray M, Armato S, Meyer C, Reeves A, McLennan G, Pais R, et al The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology. 2007;14(12):1464–1474
  29. Nappi J, Yoshida H. Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Academic Radiology. 2002;9(4):386–397
  30. Okumura T, Miwa T, Kako J, Yamamoto S, Matsumoto M, Tateno Y, et al. Variable N-Quoit filter applied for automatic detection of lung cancer by X-ray CT. Computer Assisted Radiology and Surgery (CAR98). 1998;242–247
  31. Opfer, R., Wiemker, R., 2007. Performance analysis for computer-aided lung nodule detection on LIDC data. In: Jiang, Yulei, Sahiner, Berkman (Eds.), Proceedings of the SPIE – Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, vol. 6515, p. 65151C.
  32. Papoulis A, Pillai SU. Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill; 2002;
  33. Reeves A, Biancardi A, Apanasovich T, Meyer C, MacMahon H, van Beek E, et al The lung image database consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Academic Radiology. 2007;14(12):1475–1485
  34. Reinhardt J, Higgins W. Paradigm for shape-based image analysis. Optical Engineering. 1998;37:570
  35. Rodgers J, Nicewander W, Toothaker L. Linearly independent, orthogonal, and uncorrelated variables. The American Statistician. 1984;38(2):133–134
  36. Rubin G, Lyo J, Paik D, Sherbondy A, Chow L, Leung A, et al Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology. 2005;234(1):274
  37. Sahiner B, Hadjiiski L, Chan H, Shi J, Cascade P, Kazerooni E, et al Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: observer performance study. Proceedings of SPIE. 2007;6515:65151D
  38. Schilham AMR, van Ginneken B, Loog M. Multi-scale nodule detection in chest radiographs. In:  Ellis R,  Peters T editor. Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science Series. vol. 2878:Springer; 2003;p. 602–609
  39. Schilham A, van Ginneken B, Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Medical Image Analysis. 2006;10(2):247–258
  40. Serra J. Image Analysis and Mathematical Morphology. Orlando, FL, USA: Academic Press, Inc.; 1983;
  41. Shiraishi J, Li Q, Suzuki K, Engelmann R, Doi K. Computer-aided diagnostic scheme for detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Medical Physics. 2006;33(7):2642–2653
  42. Simon G, Reid L, Tanner J, Goldstein H, Benjamin B. Growth of radiologically determined heart diameter, lung width, and lung length from 5–19 years, with standards for clinical use. Archives of Disease in Childhood. 1972;47(253):373
  43. Sonka M, Sundaramoorthy G, Hoffman E. Knowledge-based segmentation of intrathoracic airways from multidimensional high-resolution CT images. Proceedings of SPIE. 1994;2168:73–85
  44. Strickland R. Image-Processing Techniques for Tumor Detection. CRC Press; 2002;
  45. Tachibana R, Kido S. Automatic segmentation of pulmonary nodules on CT images by use of NCI lung image database consortium. Progress in Biomedical Optics and Imaging. 2006;7(30):
  46. van Ginneken, B., Armato III, S., de Hoop, B., van de Vorst, S., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A., Retico, A., Fantacci, M., et al., 2009. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Imaging Sciences Institute, University Medical Center Utrecht, The Netherlands, Technical Report. <http://anode09.isi.uu.nl/>.
  47. 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
  48. Way T, Hadjiiski L, Sahiner B, Chan H, Cascade P, Kazerooni E, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3 D active contours. Medical Physics. 2006;33(7):2323–2337
  49. Wei L, Yang Y, Nishikawa R, Jiang Y. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Transactions on Medical Imaging. 2005;24(3):371–380
  50. Wiemker R, Rogalla P, Opfer R, Ekin A, Romano V, Bülow T. Comparative performance analysis for computer aided lung nodule detection and segmentation on ultra-low-dose vs. standard-dose CT. Proceedings of SPIE. 2006;6146:614605
  51. Yuan R, Vos P, Cooperberg P. Computer-aided detection in screening CT for pulmonary nodules. American Journal of Roentgenology. 2006;186(5):1280–1287

PII: S1361-8415(10)00019-8

doi: 10.1016/j.media.2010.02.004

Medical Image Analysis
Volume 14, Issue 3 , Pages 390-406 , June 2010