Subjects are registered to a template using affine transformations. These affine transformations are used to align the tracts passing through the splenium of the corpus callosum. The tracts are represented using 3D shape context. These histogram based feature vectors are projected into a low dimensional space using non-linear Principal Component Analysis and classified using binary Support Vector Machine. Classification in DTI using Shapes of White Matter Tracts Nagesh Adluru 1, Chris Hinrichs 1, Moo K. Chung 1, Jee E. Lee 1, Vikas Singh 1 Erin D. Bigler 2, Nicholas Lange 3, Janet E. Lainhart 4, Andrew L. Alexander 1 1. University of Wisconsin-Madison, WI; 2. Brigham Young University, UT; 3. Harvard University, MA; 4. University of Utah, UT What is DTI? Diffusion Tensor Imaging (DTI) captures diffusivity of water molecules in the brain as positive semi-definite tensors. Diffusion-weighted MRI measurements in six or more directions are used to estimate the diffusion tensor field throughout the entire brain. Capturing connectivity information using DTI In white matter the diffusion is highly anisotropic. The direction of greatest diffusivity is generally assumed to be parallel to the direction of the local white matter fiber tracts. Tractography: Looking at white matter tracts in vivo By propagating/tracking along the major eigen vector of the tensors at each voxel we can visualize white matter tracts. Different tracking algorithms produce different results. Tracts have shape which can be used for group analysis in various ways: e.g. normalization, Tract Based Spatial Statistics (TBSS), Tract Based Morphometry (TBM) etc. Key novelty in this work Since shape based characterization of white matter tracts allows to capture better invariant features for high-level group analyses, we ask: Can we use shapes of white matter tracts to classify autistic subjects from controls without requiring heavy spatial normalization needed for voxel based analyses? Schematic overview of the shape based classification Shape Context based feature vectors Preliminary experimental results BACKGROUNDOVERVIEWDETAILS Left: Sample tracts passing through the splenium. A sample 2D version of binning- frame is centered at the splenium. Right: The histogram representation for the tracts. Binary Support Vector Machines Data pre-processing Left: STT, Middle: TEND, Right: Tensor line tracking. TEND trajectories for two different subjects. Yellow indicates high anisotropy, red indicates low anisotropy*. Figure taken from the Internet. * Figures taken from Lazar et. al., White Matter Tractography using Diffusion Tensor Deflection, Human Brain Mapping, Corpus callosum of the brain is believed to have important role in autism. Sample tracts (blue) passing through the splenium of the corpus callosum for two subjects. These tracts are used for feature extraction needed for the classifier. Kernel Principal Component Analysis Shape context. Demonstration in 2D for ease of understanding. (a) The log-polar binning used in 2D shape context. Sample 2D streamlines are shown as dotted blue curves. Counts for outermost radial bins are also shown. By reading the bins in the order pointed by the red curve in (a) we obtain the histogram in (b). (a) (b) * *