A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 5, 2012, Pages 1205-1218

  Doyle, Scott ^a   Feldman, Michael ^b   Tomaszewski, John ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Computer aided detection (CAD); histology; prostate cancer (CaP); quantification; supervised classification

Indexed keywords

BAYESIAN CLASSIFIER; CO-OCCURRENCE; COMPUTATIONAL TIME; COMPUTER-AIDED DETECTION; DECISION TREE CLASSIFIERS; DISEASE DETECTION; EFFICIENT ANALYSIS; ENSEMBLE METHODS; EXPERT ANNOTATIONS; FIRST-ORDER; GABOR FILTER FEATURE; GLASS SLIDES; GLEASON GRADING; HIGHER RESOLUTION; HIGHEST RESOLUTIONS; HISTOLOGY IMAGES; IMAGE FEATURES; IMAGE PYRAMIDS; INVASIVENESS; LARGE IMAGES; LOWER RESOLUTION; MULTI-RESOLUTIONS; MULTIPLE RESOLUTIONS; NEEDLE BIOPSY; PROSTATE CANCER DETECTION; PROSTATE CANCERS; QUANTIFICATION; RANDOM FOREST CLASSIFIER; RECEIVER OPERATING CHARACTERISTIC CURVES; RESOLUTION LEVEL; SECOND ORDERS; SUPERVISED CLASSIFICATION; TISSUE SAMPLES;

ADAPTIVE BOOSTING; ALGORITHMS; BIOPSY; DECISION TREES; DISEASES; HISTOLOGY; IMAGE RESOLUTION; TISSUE;

COMPUTER GRAPHICS;

AREA UNDER THE CURVE; ARTICLE; BOOSTED BAYESIAN MULTIRESOLUTION CLASSIFIER; CANCER DIAGNOSIS; CLASSIFIER; DECISION TREE; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE SEVERITY; GLEASON SCORE; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; NEEDLE BIOPSY; PROSTATE CANCER; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; BAYES THEOREM; BIOPSY, NEEDLE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PROSTATIC NEOPLASMS;

EID: 84555179528     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2010.2053540     Document Type: Article

References (37)

1. B. Matlaga, L. Eskew, and D. McCullough, "Prostate biopsy: Indications and technique," J. Urol., vol. 169, no. 1, pp. 12-19, 2003. (Pubitemid 35461538)
2. H. Welch, E. Fisher, D. Gottlieb, and M. Barry, "Detection of prostate cancer via biopsy in the medicare-seer population during the psa era," J. Nat. Cancer Inst., vol. 99, no. 18, pp. 1395-1400, 2007. (Pubitemid 351767186)
3. D. Gleason, "Classification of prostatic carcinomas," Cancer Chemother. Rep., vol. 50, no. 3, pp. 125-128, 1966.
4. D. Bostwick and I. Meiers, "Prostate biopsy and optimization of cancer yield," Eur. Urol., vol. 49, no. 3, pp. 415-417, 2006. (Pubitemid 43259544)
5. W. C. Allsbrook, K. A. Mangold, M. H. Johnson, R. B. Lane, C. G. Lane, and J. I. Epstein. (2001). Interobserver reproducibility of gleason grading of prostatic carcinoma: General pathologist. Human Pathol. [Online]. 32(1), pp. 81-88. Available: http://dx.doi.org/10.1053/hupa.2001.21135 (Pubitemid 32150632)
6. J. Epstein, P. Walsh, and F. Sanfilippo, "Clinical and cost impact of second-opinion pathology. review of prostate biopsies prior to radical prostatectomy," Amer. J. Surg. Pathol., vol. 20, no. 7, pp. 851-857, 1996. (Pubitemid 26230748)
7. G. Alexe, J. Monaco, S. Doyle, A. Basavanhally, A. Reddy, M. Seiler, S. Ganesan, G. Bhanot, and A. Madabhushi, "Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging," Exp. Biol. Med., vol. 234, pp. 860-879, 2009.
8. J. Kong, O. Sertel, H. Shimada, K. Boyer, J. Saltz, and M. Gurcan, "Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation," Pattern Recognit., vol. 42, pp. 1080-1092, 2009.
9. A. Basavanhally, S. Ganesan, S. Agner, J. Monaco, G. Bhanot, and A. Madabhushi, "Computerized image-based detection and grading of lymphocytic infiltration in her2+ breast cancer histopathology," IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 642-653, Mar. 2010.
10. D. Glotsos, I.Kalatzis, P. Spyridonos, S. Kostopoulos, A. Daskalakis, E. Athanasiadis, P. Ravazoula, G. Nikiforidis, and D. Cavourasa, "Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme," Comput. Methods Programs Biomed., vol. 90, no. 3, pp. 251-261, 2008.
11. A. Wetzel, R. Crowley, S. Kim, R. Dawson, L. Zheng, Y. Joo, Y. Yagi, J. Gilbertson, C. Gadd, D. Deerfield, and M. Becich, "Evaluation of prostate tumor grades by content based image retrieval," Proc. SPIE, vol. 3584, pp. 244-252, 1999.
12. A. Esgiar, R. Naguib, B. Sharif, M. Bennett, and A. Murray, "Fractal analysis in the detection of colonic cancer images," IEEE Trans. Inf. Technol. Biomed., vol. 6, no. 1, pp. 54-58, Mar. 2002. (Pubitemid 34491595)
13. A. Tabesh, M. Teverovskiy, H. Pang, V. Kumar, D. Verbel, A. Kotsianti, and O. Saidi, "Multifeature prostate cancer diagnosis and gleason grading of histological images," IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1366-1378, Oct. 2007. (Pubitemid 47525298)
14. J. Diamond, N. Anderson, P. Bartels, R.Montironi, and P. Hamilton, "The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia," Human Pathol., vol. 35, no. 9, pp. 1121-1131, 2004. (Pubitemid 39165345)
15. R. Farjam, H. Soltanian-Zadeh, K. Jafari-Khouzani, and R. Zoroofi, "An image analysis approach for automatic malignancy determination of prostate pathological images," Cytometry Part B (Clin. Cytometry), vol. 72, no. B, pp. 227-240, 2007. (Pubitemid 46986451)
16. P. Huang and C. Lee, "Automatic classification for pathological prostate images based on fractal analysis," IEEE Trans.Med. Imag., vol. 28, no. 7, pp. 1037-1050, Jul. 2009.
17. K. Jafari-Khouzani and H. Soltanian-Zadeh, "Multiwavelet grading of pathological images of prostate," IEEE Trans. Biomed. Eng., vol. 50, no. 6, pp. 697-704, Jun. 2003. (Pubitemid 36723603)
18. P. Viola and M. Jones, "Robust real-time face detection," Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, 2004.
19. R. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
20. D. Gabor, "Theory of communication," Proc. Inst. Electr. Eng., vol. 93, no. 26, pp. 429-457, 1946.
21. B. Manjunath and W. Ma, "Texture features for browsing and retrieval of image data," Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837- 842, 1996. (Pubitemid 126772825)
22. Y. Freund and R. Schapire, "Experiments with a new boosting algorithm," in Proc. 13th Int. Conf. Mach. Learning, 1996, pp. 148-156.
23. A. Sboner, C. Eccher, E. Blanzieri, P. Bauer, M. Cristofolini, G. Zumiani, and S. Forti, "A multiple classifier system for early melanoma diagnosis," Artif. Intell. Med., vol. 27, pp. 29-44, 2003. (Pubitemid 35418289)
24. C. Hinrichs, V. Singh, L. Mukherjee, G. Xu, M. Chung, and S. Johnson, "Spatially augmented lpboosting for ad classification with evaluations on the adni dataset," Neuroimage, vol. 48, pp. 138-149, 2009.
25. A. Madabhushi, J. Shi, M. Feldman, M. Rosen, and J. Tomaszewski, "Comparing ensembles of learners: Detecting prostate cancer from high resolution MRI," Comput. Vis. Methods Med. Image Anal., vol. LNCS 4241, pp. 25-36, 2006. (Pubitemid 44703393)
26. R. Ochs, J. Goldin, F. Abtin, H. Kim, K. Brown, P. Batra, D. Roback, M. McNitt-Gray, and M. Brown, "Automated classification of lung bronchovascular anatomy in ct using adaboost," Med. Image Anal., vol. 11, pp. 315-324, 2007. (Pubitemid 46772878)
27. L. Breiman, "Random forests," Mach. Learning, vol. 45, pp. 5-32, 2001.
28. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learning, vol. 20, pp. 273-297, 1995.
29. P. Burt and E. Adelson, "The laplacian pyramid as a compact image code," IEEE Trans. Commun., vol. COM-31, no. 4, pp. 532-540, Apr. 1983. (Pubitemid 13521088)
30. H. Cheng, X. Jiang, Y. Sun, and J. Wang, "Color image segmentation: Advances and prospects," Pattern Recognit., vol. 34, no. 12, pp. 2259- 2281, 2001.
31. A. Jain and F. Farrokhnia, "Unsupervised texture segmentation using gabor filters," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 1990, pp. 14-19.
32. R. Duda, P.Hart, andD. Stork, Pattern Classification. NewYork:Wiley, 2001.
33. N. Borley and M. Feneley, "Prostate cancer: Diagnosis and staging," Asian J. Androl., vol. 11, pp. 74-80, 2009.
34. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst., Man, Cybern., vol. SMC-9, no. 1, pp. 62-66, Jan. 1979. (Pubitemid 9413341)
35. P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Mach. Learning, vol. 63, pp. 3-42, 2006.
36. J. Quinlan, "Decision trees and decision-making," IEEE Trans. Syst.,Man Cybern., vol. 20, no. 2, pp. 339-346, Mar./Apr. 1990. (Pubitemid 20702816)
37. S. Doyle, S. Hwang, K. Shah, J. Tomaszewski, M. Feldman, and A. Madabhushi, "Automated grading of prostate cancer using architectural and textural image features," in Proc. ISBI, 2007, pp. 1284-1287. (Pubitemid 350141331)