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Intell."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p> With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver\u2019s anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex\u2013concave point for tree structured part model (CCP\u2013TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP\u2013TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method\u2019s effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method. <\/jats:p>","DOI":"10.1142\/s0218001419570052","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T04:21:35Z","timestamp":1552623695000},"page":"1957005","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Segmentation of Liver from CT Scans with CCP\u2013TSPM Algorithm"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-8683","authenticated-orcid":false,"given":"Lifang","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, P. R. China"},{"name":"Hubei Key Laboratory of Intelligent, Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei 443002, P. R. China"},{"name":"College of Software, Chongqing University of Posts and Telecommunications, Chongqing 400000, P. R. China"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400000, P. R. China"}]},{"given":"Weisheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400000, P. R. China"}]},{"given":"Shan","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, P. R. China"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Software, Chongqing University of Posts and Telecommunications, Chongqing 400000, P. R. 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