A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: Applications to histopathology image segmentation

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7623, Issue PART 1, 2010, Pages

  Xu, Jun ^a   Janowczyk, Andrew ^a,b   Chandran, Sharat ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE CONTOUR MODEL; COLOR CHANNELS; COLOR GRADIENTS; COLOR IMAGERY; COLOR IMAGES; EDGE DETECTORS; GEODESIC ACTIVE CONTOUR; GEODESIC ACTIVE CONTOUR MODEL; GRADIENT BASED; GRADIENT DETERMINATION; GRAY SCALE; LARGE IMAGES; LOCAL STRUCTURE TENSORS; MEAN SHIFT; MODEL INITIALIZATION; MULTISCALES; NORMALIZED CUTS; OBJECT BOUNDARIES; OBJECT INITIALIZATION; OBJECT OF INTERESTS; POSITIVE PREDICTIVE VALUES; REGION-BASED MODELS; SEGMENTATION RESULTS; SEGMENTATION TOOL; WEIGHTED MEAN;

COLOR; DETECTORS; GEODESY; IMAGING SYSTEMS; MEDICAL IMAGING; TARGET TRACKING; VIDEO SIGNAL PROCESSING;

IMAGE SEGMENTATION;

EID: 79751502116     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.845602     Document Type: Conference Paper

References (31)

1. A. Blake and M. Isard, Active contours: the application of techniques from graphics, vision, control theory and statistics to visual tracking of shapes in motion, Springer, 2000.
2. M. Gurcan, L. Boucheron, A. Can, A. Madabhushi, N. Rajpoot, and B. Yener, "Histopathological image analysis: A review," Biomedical Engineering, IEEE Reviews in 2, pp. 147-171, 2009.
3. A.Madabhushi, "Digital pathology image analysis: opportunities and challenges," Imaging in Medicine 1(1), pp. 7-10, 2009.
4. R.Montironi, R.Mazzuccheli, M. Scarpelli, A. Lopez-Beltran, G. Fellegara, and F. Algaba, "Gleason grading of prostate cancer in needle biopsies or radical prostatectomy specimens: contemporary approach, current clinical significance and sources of pathology discrepancies," British Journal of Urology International 95(8), pp. 1146-1152, 2005.
5. J.Meyer and C. Alvarez, "Breast carcinoma malignancy grading by bloom-richardson system vs proliferation index: reproducibility of grade and advantages of proliferation index," Modern Pathology 18(8), pp. 1067-1078, 2005.
6. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," International Journal of Computer Vision 22(1), pp. 61-79, 1997.
7. L. D. Cohen and R. Kimmel, "Global minimum for active contour models: A minimal path approach," Int. J. Comput. Vision 24(1), pp. 57-78, 1997.
8. A. Janowczyk, S. Chandran, R. Singh, D. Sasaroli, G. Coukos, M. D. Feldman, and A. Madabhushi, "Hierarchical normalized cuts: Unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays," in MICCAI, pp. 230-238, 2009.
9. T. F. Chan and L. A. Vese, "Active contours without edges," Image Processing, IEEE Trans. on 10(2), pp. 266-277, 2001.
10. L. D. Cohen, "On active contour models and balloons," CVGIP: Image Underst. 53(2), pp. 211-218, 1991.
11. R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," Pattern Analysis and Machine Intelligence, IEEE Transactions on 17(2), pp. 158-175, 1995.
12. Z. Song Chun and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on 18(9), pp. 884-900, 1996.
13. D.Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems," Communications on Pure and Applied Mathematics 42(5), pp. 577-685, 1989.
14. T. F. Chan, B. Y. Sandberg, and L. A. Vese, "Active contours without edges for vector-valued images," Journal of Visual Communication and Image Representation 11(2), pp. 130-141, 2000.
15. J. Yezzi, A., A. Tsai, and A. Willsky, "A statistical approach to snakes for bimodal and trimodal imagery," in Proc. Seventh IEEE International Conference on Computer Vision The, 2, pp. 898-903 vol.2, 1999.
16. M. Rousson and R. Deriche, "A variational framework for active and adaptative segmentation of vector valued images," in Motion and Video Computing, 2002. Proceedings. Workshop on, pp. 56-61, 2002.
17. H. Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J. E. Tomaszewski, and A. Madabhushi, "Expectation maximization driven geodesic active contour with overlap resolution (emagacor): application to lymphocyte segmentation on breast cancer histopathology," Biomedical Engineering, IEEE Transactions on , In Press.
18. S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, "Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology," in Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, pp. 284-287, May 2008.
19. S. Agner, J. Xu, H. Fatakdawala, S. Ganesan, A. Madabhushi, S. Englander, M. Rosen, K. Thomas, M. Schnall, M. Feldman, and J. Tomaszewski, "Segmentation and classification of triple negative breast cancers using dce-mri," in Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on, pp. 1227-1230, 28 2009-July 1 2009.
20. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence 24, pp. 603-619, 2002.
21. S. Jianbo and J. Malik, "Normalized cuts and image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(8), pp. 888-905, 2000.
22. Z. Tu, "Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering," in ICCV '05: Proceedings of the Tenth IEEE International Conference on Computer Vision, pp. 1589-1596, IEEE Computer Society, (Washington, DC, USA), 2005.
23. Z. Wu and R. Leahy, "An optimal graph theoretic approach to data clustering: theory and its application to image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on 15(11), pp. 1101-1113-, 1993.
24. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, "Gradient flows and geometric active contour models," pp. 810-815, 1995.
25. H.-K. Zhao, T. Chan, B. Merriman, and S. Osher, "A variational level set approach to multiphase motion," J. Comput. Phys. 127(1), pp. 179-195, 1996.
26. L. A. Vese and T. F. Chan, "A multiphase level set framework for image segmentation using the mumford and shah model," International Journal of Computer Vision 50(3), pp. 271-293, 2002.
27. I. M. Gelfand and S. V. Fomin, Calculus of variations, Dover Publications, INC., 2000.
28. G. Sapiro, "Color snakes," Computer Vision and Image Understanding 68(2), pp. 247-253, 1997.
29. L. Yang, P. Meer, and D. J. Foran, "Unsupervised segmentation based on robust estimation and color active contour models," Information Technology in Biomedicine, IEEE Transactions on 9(3), pp. 475-486, 2005.
30. C. Aldo, "Edge detection in multispectral images," pp. 40-51, Academic Press, Inc., 1991.
31. S. Di Zenzo, "A note on the gradient of a multi-image," 33(1), pp. 116-125, Academic Press Professional, Inc., San Diego, CA, USA, 1986.