Intelligent Analysis of Anatomical Shape Using Multi-sensory Interface
Identifieur interne : 005E60 ( Main/Curation ); précédent : 005E59; suivant : 005E61Intelligent Analysis of Anatomical Shape Using Multi-sensory Interface
Auteurs : Jeong-Sik Kim [Corée du Sud] ; Hyun-Joong Kim [Corée du Sud] ; Soo-Mi Choi [Corée du Sud]Source :
- Lecture Notes in Control and Information Sciences [ 0170-8643 ] ; 2006.
Descripteurs français
- Pascal (Inist)
- Algorithme apprentissage, Analyse image, Analyse morphologique, Analyse paramétrique, Analyse statistique, Anatomie, Cerveau, Encéphale, Epilepsie, Extraction forme, Forme géométrique, Homme, Intelligence artificielle, Machine exemple support, Morphoscopie, Méthode noyau, Méthode polynomiale, Stéréoscopie, Système nerveux central.
- Wicri :
- topic : Anatomie, Homme, Intelligence artificielle.
English descriptors
- KwdEn :
- Anatomy, Artificial intelligence, Brain, Central nervous system, Encephalon, Epilepsy, Geometrical shape, Human, Image analysis, Kernel method, Learning algorithm, Morphological analysis, Parametric analysis, Pattern extraction, Polynomial method, Shape analysis, Statistical analysis, Stereoscopy, Vector support machine.
Abstract
Abstract: This paper presents a method for intelligent shape analysis of the hippocampus in a human brain using multi-sensory interface. To analyze the shape difference between two groups of the hippocampus, initially we extract quantitative shape features from input images, and then perform statistical shape analysis using parametric representation and Support Vector Machines (SVMs) learning algorithm. Results suggest that the presented shape representation and a polynomial kernel based SVMs algorithm can effectively discriminate between normal controls and epilepsy patients. To provide a more immersive and realistic environment in analysis, we combined a stereoscopic display and a 6-DOF force-feedback haptic device. The presented multi-sensory environment improves space and depth perception, and provides users with sense of touch feedback while making it easier to manipulate 3D objects.
Url:
DOI: 10.1007/978-3-540-37258-5_118
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wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Kim, Hyun Joong" sort="Kim, Hyun Joong" uniqKey="Kim H" first="Hyun-Joong" last="Kim">Hyun-Joong Kim</name><affiliation wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Choi, Soo Mi" sort="Choi, Soo Mi" uniqKey="Choi S" first="Soo-Mi" last="Choi">Soo-Mi Choi</name><affiliation wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Corée du Sud</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Control and Information Sciences</title><imprint><date>2006</date></imprint><idno type="ISSN">0170-8643</idno><idno type="ISSN">0170-8643</idno></series><idno type="istex">443487A860F9AD6CE610E35DB3E4C1E9C2A4F174</idno><idno type="DOI">10.1007/978-3-540-37258-5_118</idno><idno type="ChapterID">118</idno><idno type="ChapterID">Chap118</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0170-8643</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anatomy</term><term>Artificial intelligence</term><term>Brain</term><term>Central nervous system</term><term>Encephalon</term><term>Epilepsy</term><term>Geometrical shape</term><term>Human</term><term>Image analysis</term><term>Kernel method</term><term>Learning algorithm</term><term>Morphological analysis</term><term>Parametric analysis</term><term>Pattern extraction</term><term>Polynomial method</term><term>Shape analysis</term><term>Statistical analysis</term><term>Stereoscopy</term><term>Vector support machine</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Algorithme apprentissage</term><term>Analyse image</term><term>Analyse morphologique</term><term>Analyse paramétrique</term><term>Analyse statistique</term><term>Anatomie</term><term>Cerveau</term><term>Encéphale</term><term>Epilepsie</term><term>Extraction forme</term><term>Forme géométrique</term><term>Homme</term><term>Intelligence artificielle</term><term>Machine exemple support</term><term>Morphoscopie</term><term>Méthode noyau</term><term>Méthode polynomiale</term><term>Stéréoscopie</term><term>Système nerveux central</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Anatomie</term><term>Homme</term><term>Intelligence artificielle</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This paper presents a method for intelligent shape analysis of the hippocampus in a human brain using multi-sensory interface. To analyze the shape difference between two groups of the hippocampus, initially we extract quantitative shape features from input images, and then perform statistical shape analysis using parametric representation and Support Vector Machines (SVMs) learning algorithm. Results suggest that the presented shape representation and a polynomial kernel based SVMs algorithm can effectively discriminate between normal controls and epilepsy patients. To provide a more immersive and realistic environment in analysis, we combined a stereoscopic display and a 6-DOF force-feedback haptic device. The presented multi-sensory environment improves space and depth perception, and provides users with sense of touch feedback while making it easier to manipulate 3D objects.</div></front></TEI><double idat="0170-8643:2006:Kim J:intelligent:analysis:of"><INIST><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Intelligent analysis of anatomical shape using multi-sensory interface</title><author><name sortKey="Kim, Jeong Sik" sort="Kim, Jeong Sik" uniqKey="Kim J" first="Jeong-Sik" last="Kim">Jeong-Sik Kim</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>School of Computer Engineering, Sejong University</s1><s2>Seoul</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Corée du Sud</country><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Kim, Hyun Joong" sort="Kim, Hyun Joong" uniqKey="Kim H" first="Hyun-Joong" last="Kim">Hyun-Joong Kim</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>School of Computer Engineering, Sejong University</s1><s2>Seoul</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Corée du Sud</country><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Choi, Soo Mi" sort="Choi, Soo Mi" uniqKey="Choi S" first="Soo-Mi" last="Choi">Soo-Mi Choi</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>School of Computer Engineering, Sejong University</s1><s2>Seoul</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Corée du Sud</country><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">06-0534186</idno><date when="2006">2006</date><idno type="stanalyst">PASCAL 06-0534186 INIST</idno><idno type="RBID">Pascal:06-0534186</idno><idno 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last="Kim">Hyun-Joong Kim</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>School of Computer Engineering, Sejong University</s1><s2>Seoul</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Corée du Sud</country><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Choi, Soo Mi" sort="Choi, Soo Mi" uniqKey="Choi S" first="Soo-Mi" last="Choi">Soo-Mi Choi</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>School of Computer Engineering, Sejong University</s1><s2>Seoul</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Corée du Sud</country><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Lecture notes in control and information sciences</title><idno type="ISSN">0170-8643</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Lecture notes in control and information sciences</title><idno type="ISSN">0170-8643</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anatomy</term><term>Artificial intelligence</term><term>Brain</term><term>Central nervous system</term><term>Encephalon</term><term>Epilepsy</term><term>Geometrical shape</term><term>Human</term><term>Image analysis</term><term>Kernel method</term><term>Learning algorithm</term><term>Morphological analysis</term><term>Parametric analysis</term><term>Pattern extraction</term><term>Polynomial method</term><term>Shape analysis</term><term>Statistical analysis</term><term>Stereoscopy</term><term>Vector support machine</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Forme géométrique</term><term>Analyse image</term><term>Intelligence artificielle</term><term>Stéréoscopie</term><term>Morphoscopie</term><term>Analyse morphologique</term><term>Anatomie</term><term>Cerveau</term><term>Encéphale</term><term>Homme</term><term>Système nerveux central</term><term>Extraction forme</term><term>Analyse statistique</term><term>Analyse paramétrique</term><term>Machine exemple support</term><term>Algorithme apprentissage</term><term>Méthode polynomiale</term><term>Méthode noyau</term><term>Epilepsie</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Intelligence artificielle</term><term>Anatomie</term><term>Homme</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents a method for intelligent shape analysis of the hippocampus in a human brain using multi-sensory interface. To analyze the shape difference between two groups of the hippocampus, initially we extract quantitative shape features from input images, and then perform statistical shape analysis using parametric representation and Support Vector Machines (SVMs) learning algorithm. Results suggest that the presented shape representation and a polynomial kernel based SVMs algorithm can effectively discriminate between normal controls and epilepsy patients. To provide a more immersive and realistic environment in analysis, we combined a stereoscopic display and a 6-DOF force-feedback haptic device. The presented multi-sensory environment improves space and depth perception, and provides users with sense of touch feedback while making it easier to manipulate 3D objects.</div></front></TEI></INIST><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Intelligent Analysis of Anatomical Shape Using Multi-sensory Interface</title><author><name sortKey="Kim, Jeong Sik" sort="Kim, Jeong Sik" uniqKey="Kim J" first="Jeong-Sik" last="Kim">Jeong-Sik Kim</name></author><author><name sortKey="Kim, Hyun Joong" sort="Kim, Hyun Joong" uniqKey="Kim H" first="Hyun-Joong" last="Kim">Hyun-Joong Kim</name></author><author><name sortKey="Choi, Soo Mi" sort="Choi, Soo Mi" uniqKey="Choi S" first="Soo-Mi" last="Choi">Soo-Mi Choi</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:443487A860F9AD6CE610E35DB3E4C1E9C2A4F174</idno><date when="2006" year="2006">2006</date><idno type="doi">10.1007/978-3-540-37258-5_118</idno><idno type="url">https://api.istex.fr/document/443487A860F9AD6CE610E35DB3E4C1E9C2A4F174/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000815</idno><idno type="wicri:Area/Istex/Curation">000815</idno><idno type="wicri:Area/Istex/Checkpoint">001D53</idno><idno type="wicri:doubleKey">0170-8643:2006:Kim J:intelligent:analysis:of</idno><idno type="wicri:Area/Main/Merge">006101</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Intelligent Analysis of Anatomical Shape Using Multi-sensory Interface</title><author><name sortKey="Kim, Jeong Sik" sort="Kim, Jeong Sik" uniqKey="Kim J" first="Jeong-Sik" last="Kim">Jeong-Sik Kim</name><affiliation wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Kim, Hyun Joong" sort="Kim, Hyun Joong" uniqKey="Kim H" first="Hyun-Joong" last="Kim">Hyun-Joong Kim</name><affiliation wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Choi, Soo Mi" sort="Choi, Soo Mi" uniqKey="Choi S" first="Soo-Mi" last="Choi">Soo-Mi Choi</name><affiliation wicri:level="3"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Computer Engineering, Sejong University, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Corée du Sud</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Control and Information Sciences</title><imprint><date>2006</date></imprint><idno type="ISSN">0170-8643</idno><idno type="ISSN">0170-8643</idno></series><idno type="istex">443487A860F9AD6CE610E35DB3E4C1E9C2A4F174</idno><idno type="DOI">10.1007/978-3-540-37258-5_118</idno><idno type="ChapterID">118</idno><idno type="ChapterID">Chap118</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0170-8643</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This paper presents a method for intelligent shape analysis of the hippocampus in a human brain using multi-sensory interface. To analyze the shape difference between two groups of the hippocampus, initially we extract quantitative shape features from input images, and then perform statistical shape analysis using parametric representation and Support Vector Machines (SVMs) learning algorithm. Results suggest that the presented shape representation and a polynomial kernel based SVMs algorithm can effectively discriminate between normal controls and epilepsy patients. To provide a more immersive and realistic environment in analysis, we combined a stereoscopic display and a 6-DOF force-feedback haptic device. The presented multi-sensory environment improves space and depth perception, and provides users with sense of touch feedback while making it easier to manipulate 3D objects.</div></front></TEI></ISTEX></double></record>Pour manipuler ce document sous Unix (Dilib)
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