Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing
Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using...
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| creator | Seo, Kyung-Sik Kim, Hyung-Bum Park, Taesu Kim, Pan-Koo Park, Jong-An |
| description | Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor. |
| doi_str_mv | 10.1007/11539087_135 |
| format | Conference Proceeding |
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Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540283234</identifier><identifier>ISBN: 9783540283232</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540318534</identifier><identifier>EISBN: 9783540318538</identifier><identifier>DOI: 10.1007/11539087_135</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abdominal Organ ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Contrast Enhanced Compute Tomography ; Exact sciences and technology ; Liver Region ; Manual Segmentation ; Neighboring Organ</subject><ispartof>Advances in Natural Computation, 2005, p.1027-1030</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11539087_135$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11539087_135$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>310,311,781,782,786,791,792,795,4052,4053,27932,38262,41449,42518</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17135505$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Ong, Yew Soon</contributor><contributor>Chen, Ke</contributor><contributor>Wang, Lipo</contributor><creatorcontrib>Seo, Kyung-Sik</creatorcontrib><creatorcontrib>Kim, Hyung-Bum</creatorcontrib><creatorcontrib>Park, Taesu</creatorcontrib><creatorcontrib>Kim, Pan-Koo</creatorcontrib><creatorcontrib>Park, Jong-An</creatorcontrib><title>Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing</title><title>Advances in Natural Computation</title><description>Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor.</description><subject>Abdominal Organ</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Contrast Enhanced Compute Tomography</subject><subject>Exact sciences and technology</subject><subject>Liver Region</subject><subject>Manual Segmentation</subject><subject>Neighboring Organ</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540283234</isbn><isbn>9783540283232</isbn><isbn>3540318534</isbn><isbn>9783540318538</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkE1Lw0AQhtcvsK3e_AF78SJEZ7-y2WMNVQsFBevBU5hNd2O0yZbdKPjvjdSDp-Gd92EYHkIuGFwzAH3DmBIGCl0xoQ7IVCgJghVKyEMyYTljmRDSHO0LXggu5DGZgACeGS3FKZmm9A4AXBs-Ia_zzyF0OLQ1XbVfLtJn13SuH8ZN6GnwtAz9EDENdNG_YV-7DS3XdNlh4xK9xTTmkXto0xCaiB19iqF2KbV9c0ZOPG6TO_-bM_Jyt1iXD9nq8X5ZzlfZjnMYMq_EBnPUVnrnDUeOMpfK5pLJjTGgoTbc5tqCReUL6x06tNYasbGFY8yIGbnc391hqnHr4_hlm6pdbDuM3xXToyYFauSu9lwaq75xsbIhfKSKQfWrtfqvVfwAk-FmEQ</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Seo, Kyung-Sik</creator><creator>Kim, Hyung-Bum</creator><creator>Park, Taesu</creator><creator>Kim, Pan-Koo</creator><creator>Park, Jong-An</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing</title><author>Seo, Kyung-Sik ; Kim, Hyung-Bum ; Park, Taesu ; Kim, Pan-Koo ; Park, Jong-An</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p220t-f53da6a7b4fef92a2a4645b6414d99070c92b67b0ba5f8bfeaeabbb93db8e1193</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Abdominal Organ</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Contrast Enhanced Compute Tomography</topic><topic>Exact sciences and technology</topic><topic>Liver Region</topic><topic>Manual Segmentation</topic><topic>Neighboring Organ</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Seo, Kyung-Sik</creatorcontrib><creatorcontrib>Kim, Hyung-Bum</creatorcontrib><creatorcontrib>Park, Taesu</creatorcontrib><creatorcontrib>Kim, Pan-Koo</creatorcontrib><creatorcontrib>Park, Jong-An</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Seo, Kyung-Sik</au><au>Kim, Hyung-Bum</au><au>Park, Taesu</au><au>Kim, Pan-Koo</au><au>Park, Jong-An</au><au>Ong, Yew Soon</au><au>Chen, Ke</au><au>Wang, Lipo</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing</atitle><btitle>Advances in Natural Computation</btitle><date>2005</date><risdate>2005</risdate><spage>1027</spage><epage>1030</epage><pages>1027-1030</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540283234</isbn><isbn>9783540283232</isbn><eisbn>3540318534</eisbn><eisbn>9783540318538</eisbn><abstract>Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11539087_135</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Natural Computation, 2005, p.1027-1030 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17135505 |
| source | Springer Books |
| subjects | Abdominal Organ Applied sciences Artificial intelligence Computer science control theory systems Contrast Enhanced Compute Tomography Exact sciences and technology Liver Region Manual Segmentation Neighboring Organ |
| title | Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing |
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