Nonlinear Registration For Automatic Morphometry In Schizophrenia

SCHWARZ, Daniel, Tomáš KAŠPÁREK and Ivo PROVAZNÍK. Nonlinear Registration For Automatic Morphometry In Schizophrenia. In Analysis of Biomedical Signals and Images - Proceedings of 18th Biennal International Eurasip Conference BIOSIGNAL 2006. Brno: VUTIUM Press, 2006, p. 245-247. ISBN 80-214-2120-7.

Basic information

Original name

Nonlinear Registration For Automatic Morphometry In Schizophrenia

Name in Czech

Nelineární registrace pro automatickou morfometrii u schizofrenie

Authors

SCHWARZ, Daniel, Tomáš KAŠPÁREK and Ivo PROVAZNÍK

Edition

Brno, Analysis of Biomedical Signals and Images - Proceedings of 18th Biennal International Eurasip Conference BIOSIGNAL 2006, p. 245-247, 3 pp. 2006

Publisher

VUTIUM Press

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Organization unit

Faculty of Medicine

ISBN

80-214-2120-7

Keywords in English

image processing,MRI,brain,image registration

Tags

image processing,MRI,brain,image registration

Tags

International impact, Reviewed

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Abstract

V originále

A nonlinear registration method able to handle multimodal image data is designed and used for deformation based morphometry in 3D structural MRI brain scans. The registration process is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Results of application of the method in automated spatial detection of anatomical abnormalities in first episode schizophrenia are presented.

In Czech

A nonlinear registration method able to handle multimodal image data is designed and used for deformation based morphometry in 3D structural MRI brain scans. The registration process is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Results of application of the method in automated spatial detection of anatomical abnormalities in first episode schizophrenia are presented.