title: Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.
abstract: Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.
authors.affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Weiwei
authors.initials: W
authors.lastname: Wu
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Zhuhuang
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Shuicai
authors.initials: S
authors.lastname: Wu
authors.affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000005:Abdomen / Q000473:pathology; D000465:Algorithms; D016880:Anisotropy; D016000:Cluster Analysis; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography; D003287:Contrast Media / Q000737:chemistry; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed
chemical_list: D003287:Contrast Media