Color image analysis for liver tissue classification
Yung‐Nien Sun · Optical Engineering · 1993
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.