title: Detecting structure in diffusion tensor MR images.
abstract: We derive herein first and second-order differential operators for detecting structure in diffusion tensor MRI (DTI). Unlike existing methods, we are able to generate full first and second-order differentials without dimensionality reduction and while respecting the underlying manifold of the data. Further, we extend corner and curvature feature detectors to DTI using our differential operators. Results using the feature detectors on diffusion tensor MR images show the ability to highlight structure within the image that existing methods cannot.
authors: Biomedical Signal and Image Computing Lab, University of British Columbia. kkrishna@ece.ubc.ca
authors: K Krishna
authors: KK
authors: Nand
authors: Rafeef
authors: R
authors: Abugharbieh
authors: Brian G
authors: BG
authors: Booth
authors: Ghassan
authors: G
authors: Hamarneh
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D012984:Software