HapticV1, PascalFrancis, Corpus, bibRecord, 000C22

Intelligent analysis of anatomical shape using multi-sensory interface

Identifieur interne : 000C22 ( PascalFrancis/Corpus ); précédent : 000C21; suivant : 000C23

Intelligent analysis of anatomical shape using multi-sensory interface

Auteurs : Jeong-Sik Kim ; Hyun-Joong Kim ; Soo-Mi Choi

Source :

Lecture notes in control and information sciences [ 0170-8643 ] ; 2006.

RBID : Pascal:06-0534186

Descripteurs français

Pascal (Inist)
- Forme géométrique, Analyse image, Intelligence artificielle, Stéréoscopie, Morphoscopie, Analyse morphologique, Anatomie, Cerveau, Encéphale, Homme, Système nerveux central, Extraction forme, Analyse statistique, Analyse paramétrique, Machine exemple support, Algorithme apprentissage, Méthode polynomiale, Méthode noyau, Epilepsie.

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

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0170-8643`
A05				`@2 345`
A08	`01`	`1`	`ENG`	`@1 Intelligent analysis of anatomical shape using multi-sensory interface`
A09	`01`	`1`	`ENG`	`@1 Intelligent computing in signal processing and pattern recognition : International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006`
A11	`01`	`1`		`@1 KIM (Jeong-Sik)`
A11	`02`	`1`		`@1 KIM (Hyun-Joong)`
A11	`03`	`1`		`@1 CHOI (Soo-Mi)`
A12	`01`	`1`		`@1 HUANG (De-Shuang) @9 ed.`
A12	`02`	`1`		`@1 LI (Gang) @9 ed.`
A12	`03`	`1`		`@1 IRWIN (George William) @9 ed.`
A14	`01`			`@1 School of Computer Engineering, Sejong University @2 Seoul @3 KOR @Z 1 aut. @Z 2 aut. @Z 3 aut.`
A20				`@1 945-950`
A21				`@1 2006`
A23	`01`			`@0 ENG`
A26	`01`			`@0 3-540-37257-1`
A43	`01`			`@1 INIST @2 17803 @5 354000153533951180`
A44				`@0 0000 @1 © 2006 INIST-CNRS. All rights reserved.`
A45				`@0 7 ref.`
A47	`01`	`1`		`@0 06-0534186`
A60				`@1 P @2 C`
A61				`@0 A`
A64	`01`	`1`		`@0 Lecture notes in control and information sciences`
A66	`01`			`@0 DEU`
C01	`01`		`ENG`	@0 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.
C02	`01`	`X`		`@0 001D02C`
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C03	`01`	`X`	`FRE`	`@0 Forme géométrique @5 06`
C03	`01`	`X`	`ENG`	`@0 Geometrical shape @5 06`
C03	`01`	`X`	`SPA`	`@0 Forma geométrica @5 06`
C03	`02`	`X`	`FRE`	`@0 Analyse image @5 07`
C03	`02`	`X`	`ENG`	`@0 Image analysis @5 07`
C03	`02`	`X`	`SPA`	`@0 Análisis imagen @5 07`
C03	`03`	`X`	`FRE`	`@0 Intelligence artificielle @5 08`
C03	`03`	`X`	`ENG`	`@0 Artificial intelligence @5 08`
C03	`03`	`X`	`SPA`	`@0 Inteligencia artificial @5 08`
C03	`04`	`X`	`FRE`	`@0 Stéréoscopie @5 09`
C03	`04`	`X`	`ENG`	`@0 Stereoscopy @5 09`
C03	`04`	`X`	`SPA`	`@0 Estereoscopia @5 09`
C03	`05`	`X`	`FRE`	`@0 Morphoscopie @5 18`
C03	`05`	`X`	`ENG`	`@0 Shape analysis @5 18`
C03	`05`	`X`	`SPA`	`@0 Morfoscopia @5 18`
C03	`06`	`X`	`FRE`	`@0 Analyse morphologique @5 19`
C03	`06`	`X`	`ENG`	`@0 Morphological analysis @5 19`
C03	`06`	`X`	`SPA`	`@0 Análisis morfológico @5 19`
C03	`07`	`X`	`FRE`	`@0 Anatomie @5 20`
C03	`07`	`X`	`ENG`	`@0 Anatomy @5 20`
C03	`07`	`X`	`SPA`	`@0 Anatomía @5 20`
C03	`08`	`X`	`FRE`	`@0 Cerveau @5 21`
C03	`08`	`X`	`ENG`	`@0 Brain @5 21`
C03	`08`	`X`	`SPA`	`@0 Cerebro @5 21`
C03	`09`	`X`	`FRE`	`@0 Encéphale @5 22`
C03	`09`	`X`	`ENG`	`@0 Encephalon @5 22`
C03	`09`	`X`	`SPA`	`@0 Encéfalo @5 22`
C03	`10`	`X`	`FRE`	`@0 Homme @5 23`
C03	`10`	`X`	`ENG`	`@0 Human @5 23`
C03	`10`	`X`	`SPA`	`@0 Hombre @5 23`
C03	`11`	`X`	`FRE`	`@0 Système nerveux central @5 24`
C03	`11`	`X`	`ENG`	`@0 Central nervous system @5 24`
C03	`11`	`X`	`SPA`	`@0 Sistema nervioso central @5 24`
C03	`12`	`X`	`FRE`	`@0 Extraction forme @5 25`
C03	`12`	`X`	`ENG`	`@0 Pattern extraction @5 25`
C03	`12`	`X`	`SPA`	`@0 Extracción forma @5 25`
C03	`13`	`X`	`FRE`	`@0 Analyse statistique @5 26`
C03	`13`	`X`	`ENG`	`@0 Statistical analysis @5 26`
C03	`13`	`X`	`SPA`	`@0 Análisis estadístico @5 26`
C03	`14`	`3`	`FRE`	`@0 Analyse paramétrique @5 27`
C03	`14`	`3`	`ENG`	`@0 Parametric analysis @5 27`
C03	`15`	`X`	`FRE`	`@0 Machine exemple support @5 28`
C03	`15`	`X`	`ENG`	`@0 Vector support machine @5 28`
C03	`15`	`X`	`SPA`	`@0 Máquina ejemplo soporte @5 28`
C03	`16`	`X`	`FRE`	`@0 Algorithme apprentissage @5 29`
C03	`16`	`X`	`ENG`	`@0 Learning algorithm @5 29`
C03	`16`	`X`	`SPA`	`@0 Algoritmo aprendizaje @5 29`
C03	`17`	`X`	`FRE`	`@0 Méthode polynomiale @5 30`
C03	`17`	`X`	`ENG`	`@0 Polynomial method @5 30`
C03	`17`	`X`	`SPA`	`@0 Método polinomial @5 30`
C03	`18`	`X`	`FRE`	`@0 Méthode noyau @5 31`
C03	`18`	`X`	`ENG`	`@0 Kernel method @5 31`
C03	`18`	`X`	`SPA`	`@0 Método núcleo @5 31`
C03	`19`	`X`	`FRE`	`@0 Epilepsie @5 41`
C03	`19`	`X`	`ENG`	`@0 Epilepsy @5 41`
C03	`19`	`X`	`SPA`	`@0 Epilepsia @5 41`
N21				`@1 353`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

A30	`01`	`1`	`ENG`	`@1 International Conference on Intelligent Computing @3 Kunming CHN @4 2006`

Format Inist (serveur)

NO :	PASCAL 06-0534186 INIST
ET :	Intelligent analysis of anatomical shape using multi-sensory interface
AU :	KIM (Jeong-Sik); KIM (Hyun-Joong); CHOI (Soo-Mi); HUANG (De-Shuang); LI (Gang); IRWIN (George William)
AF :	School of Computer Engineering, Sejong University/Seoul/Corée, République de (1 aut., 2 aut., 3 aut.)
DT :	Publication en série; Congrès; Niveau analytique
SO :	Lecture notes in control and information sciences; ISSN 0170-8643; Allemagne; Da. 2006; Vol. 345; Pp. 945-950; Bibl. 7 ref.
LA :	Anglais
EA :	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.
CC :	001D02C; 001D02B04
FD :	Forme géométrique; Analyse image; Intelligence artificielle; Stéréoscopie; Morphoscopie; Analyse morphologique; Anatomie; Cerveau; Encéphale; Homme; Système nerveux central; Extraction forme; Analyse statistique; Analyse paramétrique; Machine exemple support; Algorithme apprentissage; Méthode polynomiale; Méthode noyau; Epilepsie
ED :	Geometrical shape; Image analysis; Artificial intelligence; Stereoscopy; Shape analysis; Morphological analysis; Anatomy; Brain; Encephalon; Human; Central nervous system; Pattern extraction; Statistical analysis; Parametric analysis; Vector support machine; Learning algorithm; Polynomial method; Kernel method; Epilepsy
SD :	Forma geométrica; Análisis imagen; Inteligencia artificial; Estereoscopia; Morfoscopia; Análisis morfológico; Anatomía; Cerebro; Encéfalo; Hombre; Sistema nervioso central; Extracción forma; Análisis estadístico; Máquina ejemplo soporte; Algoritmo aprendizaje; Método polinomial; Método núcleo; Epilepsia
LO :	INIST-17803.354000153533951180
ID :	06-0534186

Links to Exploration step

Pascal:06-0534186

Le document en format XML

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<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>
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<fC01 i1="01" l="ENG"><s0>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.</s0>
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<s5>22</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Encephalon</s0>
<s5>22</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Encéfalo</s0>
<s5>22</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Homme</s0>
<s5>23</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Human</s0>
<s5>23</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Hombre</s0>
<s5>23</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Système nerveux central</s0>
<s5>24</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Central nervous system</s0>
<s5>24</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Sistema nervioso central</s0>
<s5>24</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Extraction forme</s0>
<s5>25</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Pattern extraction</s0>
<s5>25</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Extracción forma</s0>
<s5>25</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Analyse statistique</s0>
<s5>26</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Statistical analysis</s0>
<s5>26</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Análisis estadístico</s0>
<s5>26</s5>
</fC03>
<fC03 i1="14" i2="3" l="FRE"><s0>Analyse paramétrique</s0>
<s5>27</s5>
</fC03>
<fC03 i1="14" i2="3" l="ENG"><s0>Parametric analysis</s0>
<s5>27</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Machine exemple support</s0>
<s5>28</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Vector support machine</s0>
<s5>28</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Máquina ejemplo soporte</s0>
<s5>28</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Algorithme apprentissage</s0>
<s5>29</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Learning algorithm</s0>
<s5>29</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Algoritmo aprendizaje</s0>
<s5>29</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Méthode polynomiale</s0>
<s5>30</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Polynomial method</s0>
<s5>30</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Método polinomial</s0>
<s5>30</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Méthode noyau</s0>
<s5>31</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Kernel method</s0>
<s5>31</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Método núcleo</s0>
<s5>31</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Epilepsie</s0>
<s5>41</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Epilepsy</s0>
<s5>41</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Epilepsia</s0>
<s5>41</s5>
</fC03>
<fN21><s1>353</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
<pR><fA30 i1="01" i2="1" l="ENG"><s1>International Conference on Intelligent Computing</s1>
<s3>Kunming CHN</s3>
<s4>2006</s4>
</fA30>
</pR>
</standard>
<server><NO>PASCAL 06-0534186 INIST</NO>
<ET>Intelligent analysis of anatomical shape using multi-sensory interface</ET>
<AU>KIM (Jeong-Sik); KIM (Hyun-Joong); CHOI (Soo-Mi); HUANG (De-Shuang); LI (Gang); IRWIN (George William)</AU>
<AF>School of Computer Engineering, Sejong University/Seoul/Corée, République de (1 aut., 2 aut., 3 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in control and information sciences; ISSN 0170-8643; Allemagne; Da. 2006; Vol. 345; Pp. 945-950; Bibl. 7 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>001D02C; 001D02B04</CC>
<FD>Forme géométrique; Analyse image; Intelligence artificielle; Stéréoscopie; Morphoscopie; Analyse morphologique; Anatomie; Cerveau; Encéphale; Homme; Système nerveux central; Extraction forme; Analyse statistique; Analyse paramétrique; Machine exemple support; Algorithme apprentissage; Méthode polynomiale; Méthode noyau; Epilepsie</FD>
<ED>Geometrical shape; Image analysis; Artificial intelligence; Stereoscopy; Shape analysis; Morphological analysis; Anatomy; Brain; Encephalon; Human; Central nervous system; Pattern extraction; Statistical analysis; Parametric analysis; Vector support machine; Learning algorithm; Polynomial method; Kernel method; Epilepsy</ED>
<SD>Forma geométrica; Análisis imagen; Inteligencia artificial; Estereoscopia; Morfoscopia; Análisis morfológico; Anatomía; Cerebro; Encéfalo; Hombre; Sistema nervioso central; Extracción forma; Análisis estadístico; Máquina ejemplo soporte; Algoritmo aprendizaje; Método polinomial; Método núcleo; Epilepsia</SD>
<LO>INIST-17803.354000153533951180</LO>
<ID>06-0534186</ID>
</server>
</inist>
</record>

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Intelligent analysis of anatomical shape using multi-sensory interface

Intelligent analysis of anatomical shape using multi-sensory interface

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