Detection of fibrosis in liver biopsy images by using Bayesian classifier

In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In order to calculate the percentage of fibrosis against total liver tissue, the background area, i.e. empty slide area, is detected. Next, Bayesian classifier is used to separate fibrosis from liver tissue based on the color information. Finally, the proportion of the fibrosis area to the tissue area is computed. Experimental results show that the proposed method can estimate and detect fibrosis in the liver biopsy images with the classification accuracy of 91.42%. In addition, the average difference between the percentage of fibrosis obtained from the proposed method and that in ground truth images is 2.29 points.