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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<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>
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<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>
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