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Proceedings Paper

Color image analysis for liver tissue images
Author(s): Yung-Nien Sun; Chung-Hsien Wu; Xi-Zhang Lin; Nan-Haw Chou
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Paper Abstract

An automatic tissue characterization system is always in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or dependent on heavy human intervention and very time consuming. In this paper, we have developed a highly parallel and effective system based on color image segmentation to analyze liver tissue images. To simplify the tissue classification problem, the system first utilizes the achromatic information (the intensity) to coarsely segment the tissue image, then makes use of the chromatic information to classify the segmented regions into four different tissue classes. Thus, the proposed method includes an unsupervised probabilistic relaxation segmentation process and a supervised Bayes classification process. Because the invariant grey level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be well fulfilled at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.

Paper Details

Date Published: 14 September 1993
PDF: 12 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154531
Show Author Affiliations
Yung-Nien Sun, National Cheng Kung Univ. (Taiwan)
Chung-Hsien Wu, National Cheng Kung Univ. (Taiwan)
Xi-Zhang Lin, National Cheng Kung Univ. (Taiwan)
Nan-Haw Chou, National Cheng Kung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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