3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient...
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| description | Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time. |
| doi_str_mv | 10.1155/2017/5207685 |
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Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2017/5207685</identifier><identifier>PMID: 29090220</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Artificial intelligence ; Clustering ; Computed tomography ; Computer applications ; CT imaging ; Diagnosis ; Fuzzy algorithms ; Fuzzy logic ; Fuzzy systems ; Image processing ; Image segmentation ; Liver ; Liver cancer ; Liver tumors ; Medical imaging ; Methods ; Neural networks ; Spatial data ; Tumors ; Watersheds</subject><ispartof>BioMed research international, 2017-01, Vol.2017 (2017), p.1-11</ispartof><rights>Copyright © 2017 Weiwei Wu et al.</rights><rights>COPYRIGHT 2017 John Wiley & Sons, Inc.</rights><rights>Copyright © 2017 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.</rights><rights>Copyright © 2017 Weiwei Wu et al. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-d418306deda91e867894e263cf39c21e2df1d30014b1c3ea615270eaed35b4763</citedby><cites>FETCH-LOGICAL-c499t-d418306deda91e867894e263cf39c21e2df1d30014b1c3ea615270eaed35b4763</cites><orcidid>0000-0003-0570-8473 ; 0000-0002-8027-9499 ; 0000-0001-8203-8819</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635475/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635475/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29090220$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Corsi, Cristiana</contributor><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Zhou, Zhuhuang</creatorcontrib><creatorcontrib>Wu, Shuicai</creatorcontrib><creatorcontrib>Wu, Weiwei</creatorcontrib><creatorcontrib>Zhang, Yanhua</creatorcontrib><title>3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Clustering</subject><subject>Computed tomography</subject><subject>Computer applications</subject><subject>CT imaging</subject><subject>Diagnosis</subject><subject>Fuzzy algorithms</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver tumors</subject><subject>Medical imaging</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Spatial data</subject><subject>Tumors</subject><subject>Watersheds</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkc9v0zAUxy0EYlPZjTOyxAVphPm34wvSFLYxqYgD3dlyk5fUU-MUOyna_vq5aumAE77Ylj_6vPf8RegtJZ8olfKCEaovJCNalfIFOmWcikJRQV8ez5yfoLOU7kleJVXEqNfohBliCGPkFC34Fzz3W4h4MfVDxD-g6yGMbvRDwD7gaoFve9dBwnfJhy5fNnHYQoOvp8fHB1wV38CFhF1o8E10mxWupjG9Qa9at05wdthn6O76alF9Lebfb26ry3lRC2PGohG05EQ10DhDoVS6NAKY4nXLTc0osKalDSeEiiWtOThFJdMEHDRcLoVWfIY-772badlDU-fGo1vbTfS9iw92cN7-_RL8ynbD1krFpdAyCz4cBHH4OUEabe9TDeu1CzBMyVIjSymoyb84Q-__Qe-HKYY8XqZEyZXWjD1TnVuD9aEdct16J7WXUmpOGNUmUx_3VB2HlCK0x5Ypsbtc7S5Xe8g14-_-HPMI_04xA-d7YOVD4375_9RBZqB1zzTlWhjGnwBuS7DU</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Zhang, Rui</creator><creator>Zhou, Zhuhuang</creator><creator>Wu, Shuicai</creator><creator>Wu, Weiwei</creator><creator>Zhang, Yanhua</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0570-8473</orcidid><orcidid>https://orcid.org/0000-0002-8027-9499</orcidid><orcidid>https://orcid.org/0000-0001-8203-8819</orcidid></search><sort><creationdate>20170101</creationdate><title>3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts</title><author>Zhang, Rui ; Zhou, Zhuhuang ; Wu, Shuicai ; Wu, Weiwei ; Zhang, Yanhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499t-d418306deda91e867894e263cf39c21e2df1d30014b1c3ea615270eaed35b4763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Clustering</topic><topic>Computed tomography</topic><topic>Computer applications</topic><topic>CT imaging</topic><topic>Diagnosis</topic><topic>Fuzzy algorithms</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver tumors</topic><topic>Medical imaging</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Spatial data</topic><topic>Tumors</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Zhou, Zhuhuang</creatorcontrib><creatorcontrib>Wu, Shuicai</creatorcontrib><creatorcontrib>Wu, Weiwei</creatorcontrib><creatorcontrib>Zhang, Yanhua</creatorcontrib><collection>الدوريات العلمية والإحصائية - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Rui</au><au>Zhou, Zhuhuang</au><au>Wu, Shuicai</au><au>Wu, Weiwei</au><au>Zhang, Yanhua</au><au>Corsi, Cristiana</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2017-01-01</date><risdate>2017</risdate><volume>2017</volume><issue>2017</issue><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. 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| subjects | Algorithms Artificial intelligence Clustering Computed tomography Computer applications CT imaging Diagnosis Fuzzy algorithms Fuzzy logic Fuzzy systems Image processing Image segmentation Liver Liver cancer Liver tumors Medical imaging Methods Neural networks Spatial data Tumors Watersheds |
| title | 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts |
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