SEGMENTATION-FREE MEASURING OF CORTICAL THICKNESS FROM MRI.
Estimating the thickness of cerebral cortex is one of the most essential measurements performed in MR brain imaging. In this work we present a new approach to measure the cortical thickness which is based on minimizing line integrals over the probability map of the gray matter in the MRI volume. Previous methods often perform a pre-segmentation of the gray matter before measuring the thickness. Considering the noise and the partial volume effects, there are underlying class probabilities allocated to each voxel that this hard classification ignores, a result of which is a loss of important available information. Following the introduction of the proposed framework, the performance of our method is demonstrated on both artificial volumes and real MRI data for normal and Alzheimer affected subjects.
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Iman
I
Aganj
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Guillermo
G
Sapiro
Laboratory of Neuro Imaging, University of California-Los Angeles, School of Medicine, Los Angeles, CA 90095, USA.
Neelroop
N
Parikshak
Laboratory of Neuro Imaging, University of California-Los Angeles, School of Medicine, Los Angeles, CA 90095, USA.
Sarah K
SK
Madsen
Laboratory of Neuro Imaging, University of California-Los Angeles, School of Medicine, Los Angeles, CA 90095, USA.
Paul M
PM
Thompson
Cortical thickness measurement; gray matter density; magnetic resonance imaging; soft classification