Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

• Published in: Computational and mathematical methods in medicine Vol. 2016; no. 2016; pp. 1 - 14
• Main Authors: Zhang, Yanhua, Wu, Shuicai, Zhou, Zhuhuang, Wu, Weiwei
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
• Subjects: Abdomen - pathology; Algorithms; Anisotropy; Cluster Analysis; Computer Simulation; Cone-Beam Computed Tomography; Contrast Media - chemistry; Databases, Factual; Diagnosis, Computer-Assisted; Humans; Image Processing, Computer-Assisted - methods; Liver - diagnostic imaging; Liver - pathology; Models, Statistical; Normal Distribution; Pattern Recognition, Automated; Probability; Reproducibility of Results; Tomography, X-Ray Computed
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2016/9093721

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.