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


Affiliations
1 College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
2 College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
 

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despitemany 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.
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  • Automatic Liver Segmentation on Volumetric CT Images using Supervoxel-Based Graph Cuts

Abstract Views: 84  |  PDF Views: 4

Authors

Weiwei Wu
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
Zhuhuang Zhou
College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
Shuicai Wu
College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
Yanhua Zhang
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

Abstract


Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despitemany 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.