Markov random field method for dynamic PET image segmentation

Kang-Ping Lin; Shyhliang A. Lou; Chin-Lung Yu; Being-Tau Chung; Liang-Chi Wu; Ren-Shyan Liu

doi:10.1117/12.310847

24 June 1998 Markov random field method for dynamic PET image segmentation

Kang-Ping Lin, Shyhliang A. Lou, Chin-Lung Yu, Being-Tau Chung, Liang-Chi Wu, Ren-Shyan Liu

Author Affiliations +

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310847
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

In this paper, the Markov random field (MRF) clustering method for highly noisy medical image segmentation is presented. In MRF method, the image to be segmented is analyzed in a probabilistic way that establishes image model by a posteriori probability density function with Bayes' theorem, with relation between pixel positions as well as gray-levels involved. The adaptive threshold parameter is determined in the iterative clustering process to achieve global optimal segmentation. The presented method and other segmentation methods in use are tested on simulation images of different noise levels, and the numerical comparison result is presented. It also is applied on the highly noisy positron emission tomography images, in that the diagnostic hypoxia fraction is automatically calculated. The experimental results are acceptable, and show that the presented method is suitable and robust for noisy image segmentation.

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Kang-Ping Lin, Shyhliang A. Lou, Chin-Lung Yu, Being-Tau Chung, Liang-Chi Wu, and Ren-Shyan Liu "Markov random field method for dynamic PET image segmentation", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310847

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