Automatic Segmentation of Liver from CT Scans with CCP–TSPM Algorithm

Abstract

With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver’s anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex–concave point for tree structured part model (CCP–TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP–TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method’s effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method.