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Proceedings Paper

Markov random field method for dynamic PET image segmentation
Author(s): Kang-Ping Lin; Shyhliang A. Lou; Chin-Lung Yu; Being-Tau Chung; Liang-Chi Wu; Ren-Shyan Liu
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Paper 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.

Paper Details

Date Published: 24 June 1998
PDF: 7 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310847
Show Author Affiliations
Kang-Ping Lin, Chung Yuan Univ. (Taiwan)
Shyhliang A. Lou, Univ. of California/San Francisco School of Medicine (Taiwan)
Chin-Lung Yu, Chung Yuan Univ. (Taiwan)
Being-Tau Chung, Chung Yuan Univ. (Taiwan)
Liang-Chi Wu, Taipei Veterans General Hospital (Taiwan)
Ren-Shyan Liu, Taipei Veterans General Hospital (Taiwan)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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