Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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 magnetic resonance imaging (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.

Anglický abstrakt

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 magnetic resonance imaging (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.

BibTex


@article{BUT45079,
  author="Daniel {Schwarz} and Ivo {Provazník}",
  title="A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research",
  annote="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 magnetic
resonance imaging (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.",
  address="IEEE",
  chapter="45079",
  institution="IEEE",
  journal="IEEE TRANSACTIONS ON MEDICAL IMAGING",
  number="4",
  volume="26",
  year="2007",
  month="april",
  pages="452--461",
  publisher="IEEE",
  type="journal article - other"
}