Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps
2013
Our
purpose in this study was to develop an automated method for measuring
three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance
(MR) images. Our proposed method consists of mainly three steps. First,
a brain parenchymal region was segmented based on brain model matching. Second,
a 3D fuzzy membership map for a cerebral cortical region was created by
applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images.
Third, cerebral cortical thickness was three- dimensionally measured on each
cortical surface voxel by using a localized gradient vector trajectory in a
fuzzy membership map. Spherical models with 3 mm artificial cortical regions,
which were produced using three noise levels of 2%, 5%, and 10%, were employed
to evaluate the proposed method. We also applied the proposed method to
T1-weighted images obtained from 20 cases, i.e.,
10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The
thicknesses of the 3 mm artificial cortical regions for spherical models with
noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ±
0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean
thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients
and 3.3 ± 0.4 mm for CN subjects, respectively (p
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