Publication details

 

A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research

Basic information
Original title:A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research
Authors:Daniel Schwarz, Tomáš Kašpárek, Ivo Provazník, Jiří Jarkovský
Further information
Citation:SCHWARZ, Daniel, Tomáš KAŠPÁREK, Ivo PROVAZNÍK a Jiří JARKOVSKÝ. A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research. IEEE Transactions on Medical Imaging, New York: IEEE, 2007, roč. 26, č. 4, s. 452-461. ISSN 0278-0062.Export BibTeX
@article{717686,
author = {Schwarz, Daniel and Kašpárek, Tomáš and Provazník, Ivo and Jarkovský, Jiří},
article_location = {New York},
article_number = {4},
keywords = {MRI;image processing;schizophrenia;image registration;computational neuroanatomy},
language = {eng},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
title = {A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research},
url = {http://dx.doi.org/10.1109/TMI.2007.892512},
volume = {26},
year = {2007}
}
Original language:English
Field:Neurology, neurosurgery, neurosciences
WWW:link to a new windowhttp://dx.doi.org/10.1109/TMI.2007.892512
Type:Article in Periodical
Keywords:MRI;image processing;schizophrenia;image registration;computational neuroanatomy

Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching MRI brain image data obtained from various subjects and with various imaging conditions are solved here. Registration 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. Five similarity measures are tested in an experiment with image data obtained from the Simulated Brain Database and a quantitative evaluation of the algorithm is presented. Results of application of the method in automated spatial detection of anatomical abnormalities in first-episode schizophrenia are presented.

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