A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research.
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.
Schwarz
Daniel
D
Masaryk University, Institute of Biostatistics and Analyses, 625 00 Brno, Czech Republic. schwarz@iba.muni.cz
Kasparek
Tomas
T
Provaznik
Ivo
I
Jarkovsky
Jiri
J
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D003198:Computer Simulation; D004548:Elasticity; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009445:Neuroanatomy / Q000379:methods; D009462:Neurology / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011570:Psychiatry / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique