Boundary refined texture segmentation on liver biopsy images for quantitative assessment of fibrosis severity

We applied a new texture segmentation algorithm to improve the segmentation of boundary areas for distinction on the liver needle biopsy images taken from microscopes for automatic assessment of liver fibrosis severity. It was difficult to gain satisfactory segmentation results on the boundary areas of textures with some of existing texture segmentation algorithms in our preliminary experiments. The proposed algorithm consists of three steps. The first step is to apply the K-View-datagram segmentation method to the image. The second step is to find a boundary set which is defined as a set including all the pixels with more than half of its neighboring pixels being classified into clusters other than that of itself by the K-View-datagram method. The third step is to apply a modified K-view template method with a small scanning window to the boundary set to refine the segmentation. The algorithm was applied to the real liver needle biopsy images provided by the hospitals in Wuhan, China. Initial experimental results show that this new segmentation algorithm gives high segmentation accuracy and classifies the boundary areas better than the existing algorithms. It is a useful tool for automatic assessment of liver fibrosis severity.