Probabilistic pairwise Markov models: Application to prostate cancer detection

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7259, Issue , 2009, Pages

  Monaco, James ^a   Tomaszewski, John E ^b   Feldman, Michael D ^b   Moradi, Mehdi ^c   Mousavi, Parvin ^c   Boag, Alexander ^c   Davidson, Chris ^c   Abolmaesumi, Purang ^c   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c QUEEN S UNIVERSITY   (Canada)

Author keywords

Classification; Histology; Markov Random Fields (MRFs); Prostate cancer; Statistical methods

Indexed keywords

BAYESIAN FRAMEWORKS; CAD SYSTEM; CONDITIONAL PROBABILITY DENSITY; CONTEXTUAL INFORMATION; ENTIRE SYSTEM; EQUIVALENCE THEOREM; FUNCTIONAL FORMS; GIBBS DISTRIBUTION; HEURISTIC MODEL; HISTOLOGICAL SECTION; JOINT PROBABILITY DENSITY FUNCTION; MARKOV MODEL; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS (MRFS); MATHEMATICAL ABSTRACTION; MODELING CAPABILITIES; MRF FRAMEWORK; POTENTIAL FUNCTION; POTTS MODELS; PROSTATE CANCER; PROSTATE CANCER DETECTION; PROSTATE CANCERS; RECEIVER OPERATOR CHARACTERISTICS;

BIOLOGICAL RADIATION EFFECTS; COMPUTER AIDED DESIGN; COMPUTER AIDED DIAGNOSIS; HIDDEN MARKOV MODELS; HISTOLOGY; IMAGE SEGMENTATION; IMAGING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL OPERATORS; MEDICAL IMAGING; PROBABILITY DISTRIBUTIONS; STATISTICS; STRUCTURAL FRAMES; WIRELESS TELECOMMUNICATION SYSTEMS;

PROBABILITY DENSITY FUNCTION;

EID: 71749093569     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.812462     Document Type: Conference Paper

References (28)

1. Pappas, T. N., "An adaptive clustering algorithm for image segmentation," IEEE Transaction on Signal Processing 40, 901-914 (April 1992).
2. Besag, J., "On the statistical analysis of dirty pictures," Journal of the Royal Statistical Society. Series B (Methodological) 48(3), 259-302 (1986).
3. Figueiredo, M. A. T. and Leitao, J. M. N., "Unsupervised image restoration and edge location using compound gauss-markov random fields and the mdl principle," IEEE Transactions on Image Processing 6, 1089-1102 (Aug. 1997).
4. Paget, R. and Longstaff, I. D., "Texture synthesis via a noncausal nonparametric multiscale markov random field," IEEE Transactions on Image Processing 7, 925-931 (June 1998).
5. Zalesny, A. and Gool, L. J. V., "A compact model for viewpoint dependent texture synthesis," in [SMILE Workshop], 124-143, Springer-Verlag, London, UK (2001).
6. Farag, A., El-Baz, A., and Gimel'farb, G., "Precise segmentation of multimodal images," IEEE Transactions on Image Processing 15, 952-968 (April 2006).
7. Kato, Z. and Pong, T., "A markov random field image segmentation model for color textured images," Image and Vision Computing 24(10), 1103 - 1114 (2006).
8. Awate, S., Tasdizen, T., and Whitaker, R., "Unsupervised texture segmentation with nonparametric neighborhood statistics," in [Computer Vision ECCV], 494-507 (2006).
9. Bouman, C. A. and Shapiro, M., "A multiscale random field model for bayesian image segmentation," IEEE Transactions on Image Processing 3, 162-177 (March 1994).
10. Wu, J. and Chung, A. C. S., "A segmentation model using compound markov random fields based on a boundary model," IEEE Transactions on Image Processing 16, 241-252 (Jan. 2007).
11. Besag, J., "Spatial interaction and the statistical analysis of lattice systems," Journal of the Royal Statistical Society. Series B (Methodological) 36(2), 192-236 (1974).
12. Geman, S. and Geman, D., "Stochastic relaxation, gibbs distribution, and the bayesian restoration of images," IEEE Transactions on Pattern Recognition and Machine Intelligence 6, 721-741 (1984).
13. Levada, A., Mascarenhas, N., and Tannus, A., "Pseudolikelihood equations for potts mrf model parameter estimation on higher order neighborhood systems," IEEE Geoscience and Remote Sensing Letters 5, 522-526 (July 2008).
14. Begelman, G., Gur, E., Rivlin, E., Rudzsky, M., and Zalevsky, Z., "Cell nuclei segmentation using fuzzy logic engine," in [ICIP], V: 2937-2940 (2004).
15. Doyle, S., Hwang, M., Shah, K., Madabhushi, A., Feldman, M., and Tomaszeweski, J., "Automated grading of prostate cancer using architectural and textural image features," in [ISBI], 1284-1287 (12-15 April 2007).
16. Gao, M., Bridgman, P., and Kumar, ., "Computer-aided prostate cancer diagnosis using image enhancement and jpeg2000," Applications of Digital Image Processing XXVI 5203(1), 323-334, SPIE (2003).
17. Doyle, S., Madabhushi, A., Feldman, M., and Tomaszeweski, J., "A boosting cascade for automated detection of prostate cancer from digitized histology," MICCAI , 504-511 (2006).
18. Kumar, V., Abbas, A., and Fausto, N., [Robbins and Cotran Pathologic Basis of Disease], Saunders (2004).
19. Duda, R., Hart, P., and Stork, D., [Pattern Classification], John Wiley & Sons (2001).
20. Banerjee, S., Carlin, B., and Gelfand, A., [Hierarchical Modeling and Analysis for Spatial Data], CRC Press (2004).
21. Grimmett, G., "A theorem about random fields," Bulletin of the London Mathematical Society 5(13), 81-84 (1973).
22. Moussouris, J., "Gibbs and markov random systems with constraints," Journal of Statistical Physics 10, 11-33 (Jan. 1974).
23. Geman, D., "Random fields and inverse problems in imaging," in [Lecture Notes in Mathematics], 1427, 113-193, Springer-Verlag (1991).
24. Jain, A. K., [Fundamentals of Digital Image Processing], Prentice Hall (1989).
25. Hojjatoleslami, S. and Kittler, J., "Region growing: a new approach," IEEE Trans. on Image Processing 7, 1079-1084 (July 1998).
26. Geman, S. and Graffigne, C., "Markov random field image models and their applications to computer vision," in [Proceedings of the International Congress of Mathematicians], 1496-1517 (1986).
27. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., "A comparative study of energy minimization methods for markov random fields," in [ECCV], 16-29 (2006).
28. Dubes, R., Jain, A., Nadabar, S., and Chen, C., "Mrf model-based algorithms for image segmentation," in [Proceedings of the10th International Conference on Pattern Recognition], i, 808-814 vol.1 (1990).