Using Prior Shape and Points in Medical Image Segmentation
In this paper we present a new variational framework in level set form for image segmentation, which incorporates both a prior shape and prior fixed locations of a small number of points. The idea underlying the model is the creation of two energy terms in the energy function for the geodesic active...
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| creator | Chen, Yunmei Guo, Weihong Huang, Feng Wilson, David Geiser, Edward A. |
| description | In this paper we present a new variational framework in level set form for image segmentation, which incorporates both a prior shape and prior fixed locations of a small number of points. The idea underlying the model is the creation of two energy terms in the energy function for the geodesic active contours. The first energy term is for the shape, the second for the locations of the points In this model, segmentation is achieved through a registration technique, which combines a rigid transformation and a local deformation. The rigid transformation is determined explicitly by using shape information, while the local deformation is determined implicitly by using image gradients and prior locations. We report experimental results on both synthetic and ultrasound images. These results compared with the results obtained by using a previously reported model, which only incorporates a shape prior into the active contours. |
| doi_str_mv | 10.1007/978-3-540-45063-4_19 |
| format | Book Chapter |
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The rigid transformation is determined explicitly by using shape information, while the local deformation is determined implicitly by using image gradients and prior locations. We report experimental results on both synthetic and ultrasound images. These results compared with the results obtained by using a previously reported model, which only incorporates a shape prior into the active contours.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540404989</identifier><identifier>ISBN: 3540404988</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540450634</identifier><identifier>EISBN: 3540450637</identifier><identifier>DOI: 10.1007/978-3-540-45063-4_19</identifier><identifier>OCLC: 953665297</identifier><identifier>LCCallNum: TA1634</identifier><language>eng</language><publisher>Germany: Springer Berlin / Heidelberg</publisher><subject>active contours ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; energy minimization ; Exact sciences and technology ; level set methods ; Pattern recognition. Digital image processing. 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The first energy term is for the shape, the second for the locations of the points In this model, segmentation is achieved through a registration technique, which combines a rigid transformation and a local deformation. The rigid transformation is determined explicitly by using shape information, while the local deformation is determined implicitly by using image gradients and prior locations. We report experimental results on both synthetic and ultrasound images. These results compared with the results obtained by using a previously reported model, which only incorporates a shape prior into the active contours.</description><subject>active contours</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>energy minimization</subject><subject>Exact sciences and technology</subject><subject>level set methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Prior shape and points</subject><subject>Software</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540404989</isbn><isbn>3540404988</isbn><isbn>9783540450634</isbn><isbn>3540450637</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2003</creationdate><recordtype>book_chapter</recordtype><recordid>eNpFkE1PwzAMhsOnqMb-AYdeOAacOP0wN4T4mDTEpLFzlLZZKWxtScqBf0-6TcKyZOm1_cp-GLsScCMAslvKco48UcBVAilypQUdsWmQMYg7TR2zSKRCcERFJ_89UJTTKYsAQXLKFJ6ziBJM00RSdsGm3n9CCJSSQEXsbuWbto4XrulcvPwwvY1NW8WLrmkHHzdt_GqrpjSbeLY1tY2Xtt7adjBD07WX7GxtNt5OD3XCVk-P7w8vfP72PHu4n_NeZnLgZUUiTwohKRxYoajAgApZVpWRKRWWTKXKXGFRUGoKAescRGGEIYGQ5gIn7Hrv2xsfLlk705aN171rtsb9apGkmcwhCXNyP-dDq62t00XXfXktQI9QdSCkUQdGegdQj1DDEh7MXff9Y_2g7bhVhied2ZQByGCd1wh5hkijlyTCPxIocs4</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Chen, Yunmei</creator><creator>Guo, Weihong</creator><creator>Huang, Feng</creator><creator>Wilson, David</creator><creator>Geiser, Edward A.</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2003</creationdate><title>Using Prior Shape and Points in Medical Image Segmentation</title><author>Chen, Yunmei ; Guo, Weihong ; Huang, Feng ; Wilson, David ; Geiser, Edward A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p272t-cd9185b129835d31d0a04a04cdda269be9ad4c843bb96ab10f801ba1a91306813</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2003</creationdate><topic>active contours</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>energy minimization</topic><topic>Exact sciences and technology</topic><topic>level set methods</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Prior shape and points</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yunmei</creatorcontrib><creatorcontrib>Guo, Weihong</creatorcontrib><creatorcontrib>Huang, Feng</creatorcontrib><creatorcontrib>Wilson, David</creatorcontrib><creatorcontrib>Geiser, Edward A.</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Yunmei</au><au>Guo, Weihong</au><au>Huang, Feng</au><au>Wilson, David</au><au>Geiser, Edward A.</au><au>Zerubia, Josiane</au><au>Figueiredo, Mário A. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Energy Minimization Methods in Computer Vision and Pattern Recognition, 2003, Vol.2683, p.291-305 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_15672805 |
| source | Springer Books |
| subjects | active contours Applied sciences Artificial intelligence Computer science control theory systems energy minimization Exact sciences and technology level set methods Pattern recognition. Digital image processing. Computational geometry Prior shape and points Software |
| title | Using Prior Shape and Points in Medical Image Segmentation |
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