1 July 1993 Color image analysis for liver tissue classification
Author Affiliations +
Automatic tissue characterization systems are in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or are dependent on heavy human intervention and very time consuming. 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 segment the tissue image coarsely, 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 gray level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be fulfilled well at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.
Yung-Nien Sun, Yung-Nien Sun, Chung-Hsien Wu, Chung-Hsien Wu, Xi-Zhang Lin, Xi-Zhang Lin, Nan-Haw Chou, Nan-Haw Chou, } "Color image analysis for liver tissue classification," Optical Engineering 32(7), (1 July 1993). https://doi.org/10.1117/12.138574 . Submission:


Theory and applications of frequency image of color vectors
Proceedings of SPIE (February 02 2009)
Region-based color image segmentation scheme
Proceedings of SPIE (December 27 1998)
Color image analysis for liver tissue images
Proceedings of SPIE (September 13 1993)

Back to Top