A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research
Language English Country United States Media print
Document type Evaluation Study, Journal Article, Research Support, Non-U.S. Gov't
- MeSH
- Algorithms * MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Models, Neurological MeSH
- Brain anatomy & histology physiology MeSH
- Neuroanatomy methods MeSH
- Neurology methods MeSH
- Computer Simulation MeSH
- Elasticity MeSH
- Psychiatry methods MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Sensitivity and Specificity MeSH
- Subtraction Technique MeSH
- Artificial Intelligence * MeSH
- Image Enhancement methods MeSH
- Imaging, Three-Dimensional methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
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