Generalized Surface Flows for Deformable Registration and Cortical Matching
Identifieur interne : 000231 ( Main/Exploration ); précédent : 000230; suivant : 000232Generalized Surface Flows for Deformable Registration and Cortical Matching
Auteurs : I. Eckstein [États-Unis] ; A. Joshi [États-Unis] ; J. Kuo [États-Unis] ; R. Leahy [États-Unis] ; M. Desbrun [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: Despite being routinely required in medical applications, deformable surface registration is notoriously difficult due to large intersubject variability and complex geometry of most medical datasets. We present a general and flexible deformable matching framework based on generalized surface flows that efficiently tackles these issues through tailored deformation priors and multiresolution computations. The value of our approach over existing methods is demonstrated for automatic and user-guided cortical registration.
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DOI: 10.1007/978-3-540-75757-3_84
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Eckstein</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, University of Southern California</wicri:regionArea><placeName><settlement type="city">Los Angeles</settlement><region type="state">Californie</region></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author><author><name sortKey="Joshi, A" sort="Joshi, A" uniqKey="Joshi A" first="A." last="Joshi">A. Joshi</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Signal and Image Processing Institute, University of Southern California</wicri:regionArea><placeName><settlement type="city">Los Angeles</settlement><region type="state">Californie</region></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author><author><name sortKey="Kuo, J" sort="Kuo, J" uniqKey="Kuo J" first="J." last="Kuo">J. 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Desbrun</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Caltech</wicri:regionArea><wicri:noRegion>Caltech</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2007</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">C82DFE454C97C87CEB4F399167FF1D9E03997B7C</idno><idno type="DOI">10.1007/978-3-540-75757-3_84</idno><idno type="ChapterID">84</idno><idno type="ChapterID">Chap84</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Despite being routinely required in medical applications, deformable surface registration is notoriously difficult due to large intersubject variability and complex geometry of most medical datasets. 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