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

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          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.

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          Most cited references38

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          A Threshold Selection Method from Gray-Level Histograms

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            An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

            After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
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              The expectation-maximization algorithm

              T.K. Moon (1996)
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                Author and article information

                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                CMMM
                Computational and Mathematical Methods in Medicine
                Hindawi Publishing Corporation
                1748-670X
                1748-6718
                2016
                5 April 2016
                : 2016
                : 9093721
                Affiliations
                1College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
                2College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
                Author notes

                Academic Editor: Marc Thilo Figge

                Article
                10.1155/2016/9093721
                4835633
                27127536
                30327681-9b57-49c5-8e42-3c9eb16e010e
                Copyright © 2016 Weiwei Wu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 December 2015
                : 20 February 2016
                : 8 March 2016
                Categories
                Research Article

                Applied mathematics
                Applied mathematics

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