Learning based random walks for automatic liver segmentation in CT image

Liver segmentation from Computed Tomography (CT) image is important for the diagnosis and intervention of liver diseases. In this paper, we propose an automatic liver segmentation method based on probability image and random walks. First, pixel-level texture features are extracted and liver probability images are generated corresponding to the test images using a binary classification approach. Second, random walk algorithm with automatic seed points is developed to detect the liver region. The proposed method is validated on standard data with five evaluation criteria. Experimental results demonstrate the effectiveness and robustness of the proposed method for the liver segmentation in CT image. The proposed method can achieve an average volumetric overlap error of 8.76% and an average surface distance of 1.30 mm.