Automatic liver segmentation based on appearance and context information

Background: Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis. Methods: This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction. Results: The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system. Conclusions: Experimental results show that the proposed method is superior to eight other state of the art methods.