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Lesion Detection in Cervigrams based on Boundary cues

R. Jeyanthi

Abstract


In this paper, I have focused on the extraction and segmentation of a specific tissue within the cervix, known as the AcetoWhite(AW) tissue that turns white after the application of 3% to 5% of acetic acid during cervicography. The AW is a major indicator of cervical cancer. This paper presents an automated approach to detect and segment AW lesions by learning class-specific boundaries. The approach is a multi-step scheme that includes the watershed transform that converts the image into an edge map[10] that contains the lesion boundary and viewing the watershed map as a Markov random field (MRF), in which each watershed superpixel corresponds to a binary random variable indicating whether the superpixel is part of the lesion. By applying a belief-propagation (BP) algorithm on the loopy MRF, the final lesion region segmentation is obtained.

Keywords


Belief Propagation, Cervigrams, Markov Random Field, Superpixel, Watershed Transform.

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References


P. M. Cristoforoni, D. Gerbaldo, A. Perino, R. Piccoli, F. J. Montz, and G. L. Captianio, ―Computerized colposcopy: Results of a pilot study and analysis of its clinical relevance,‖ Obstet. Gynecol., vol. 85, pp. 1011–1016, 1995.

L. Fei-Fei and P. Perona, ―A Bayesian hierarchical model for learning natural scene categories,‖ in Proc. Comput. Vis. Pattern Recogni (CVPR), 2005, pp. 524–531.

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, ―Content analysis of uterine cervix images: Initial steps towards content based indexing and retrieval of cervigrams,‖ in Proc. SPIE Med. Imag., 2006, vol. 6144, pp. 1549–1556.

H. Greenspan, S. Gordon, G. Zimmerman, S. Lotenberg, J. Jeronimo, S. Antani, and R. Long, ―Automatic detection of anatomical landmarks in uterine cervix images,‖ in Proc. IEEE Trans. Med. Imag., 2009, pp. 454–468.

X. Huang, W. Wang, Z. Xue, S. Antani, L. R. Long, and J.Jeronimo, ―Tissue classification using cluster features for lesion detection in digital cervigrams,‖ in Proc. SPIE Med. Imag., 2008, pp. 69141Z.1–69141Z.8.

Q. Ji, J. Engel, and E. Craine, ―Texture analysis for classification of cervix lesions,‖ IEEE Trans. Med. Imag., vol. 19, no. 11, pp. 1144–1149, Nov. 2000.

T. C. Wright Jr., ―Cervical cancer screening using visualization techniques,‖ J. Nat. Cancer Inst. Monogr., vol. 31, pp. 66–71, 2003

R. Kindermann and J. L. Snell, Markov Random Fields and Their Applications.Providence, RI: American Mathematical Society, 1980.

H. Lange, ―Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix,‖ in Proc. SPIE Med. Imag., 2005, vol. 5747, pp. 1004–1017.

J. Park and J. M. Keller, ―Snakes on the watershed,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 10, pp. 1201–1205, Oct. 2001.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufmann, 1988.

B. W. Pogue, M. A. Mycek, and D. Harper, ―Image analysis for discrimination of cervical neoplasia,‖ J. iomed. Opt., vol. 5, no. 1, pp. 72–82, 2000.

M. Prasad, A. Zisserman, A. W. Fitzgibbon, M. P. Kumar, and P. H. S. Torr, ―Learning class-specific edges for object detection and segmentation,‖ in Proc. Indian Conf. Comput. Vis., Graph. Image Process., 2006,pp. 94–105.

J. W. Sellors and R. Sankaranarayanan, Colposcopy and Treatment of Cervical Intraepithelial Neoplasia: A Beginner’s Manual. Lyon, France: International Agency for Research on Cancer, 2003.

Y. Srinivasan, F. Gao, B. Tulpule, S. Yang, S. Mitra, and B. Nutter, ―Segmentation and classification of cervix lesions by texture and pattern analysis,‖ Int. J. Intell. Syst. Technol. Appl., vol. 1, no. 3/4, pp.234–246, 2006.

A. Stafl, ―Cervicography: A new method for cervical cancer detection,‖ Amer. J. Obstet. Gynecol., vol. 139, no. 7, pp. 815–825, 1981.

L. Vincent and P. Soille, ―Watersheds in digital spaces: An efficient algorithm based on immersion simulations,‖ in Proc. IEEE Trans. Pattern Anal. Mach. Intell., 1991, pp. 583–598.


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