Segmentation-free measurement of cortical thickness from MRI

Iman Aganj, Guillermo Sapiro, Neelroop Parikshak, Sarah K. Madsen, Paul M. Thompson
2008 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Fig. 1. Results on an artificial probability map. Inner and outer surfaces of a paraboloid-shaped layer of GM are depicted. Line segments are chosen by the algorithm such that they give the smallest integrals (of the probability map) among all line segments passing through every selected test point, shown as small circles. ABSTRACT Estimating the thickness of cerebral cortex is a key step in many MR brain imaging studies, revealing valuable information on development or disease progression. In
more » ... his work we present a new approach to measure the cortical thickness, based on minimizing line integrals over the probability map of the gray matter in the MRI volume. Previous methods often perform a binary-valued segmentation of the gray matter before measuring the thickness. e of image noise and partial voluming, such a hard lassification ignores the underl ass probabilities assigned to each voxel, d tially useful information. ickness over time have revealed the trajectory of diseases in the with age, ognitive deterioration, genotype, or medication. Various approach roposed to automate is cortical thickness measurement from Magnetic Resonance ebrospinal fluid he input to the ma Becaus c ying tissue cl iscarding poten We describe our proposed method and demonstrate its performance on both artificial volumes and real 3D brain MRI data from subjects with Alzheimer's disease and healthy individuals.
doi:10.1109/isbi.2008.4541324 pmid:25741407 pmcid:PMC4346190 dblp:conf/isbi/AganjSPMT08 fatcat:bcro6dj6jvbwhl5su74mwyqseu