Nonlinear Registration For Automatic Morphometry In Schizophrenia

SCHWARZ, Daniel; Tomáš KAŠPÁREK a 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, s. 245-247. ISBN 80-214-2120-7.

Základní údaje

Originální název

Nonlinear Registration For Automatic Morphometry In Schizophrenia

Název česky

Nelineární registrace pro automatickou morfometrii u schizofrenie

Autoři

SCHWARZ, Daniel; Tomáš KAŠPÁREK a Ivo PROVAZNÍK

Vydání

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

Nakladatel

VUTIUM Press

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Označené pro přenos do RIV

Ne

Organizační jednotka

Lékařská fakulta

ISBN

80-214-2120-7

Klíčová slova anglicky

image processing,MRI,brain,image registration

Štítky

image processing,MRI,brain,image registration

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

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.

Česky

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.