DTI segmentation using an information theoretic tensor dissimilarity measure. Export

@article{citeulike:849762, abstract = {In recent years, diffusion tensor imaging (DTI) has become a popular in vivo diagnostic imaging technique in Radiological sciences. In order for this imaging technique to be more effective, proper image analysis techniques suited for analyzing these high dimensional data need to be developed. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of DTI. In a DTI, the symmetric positive definite (SPD) diffusion tensor at each voxel can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the Kullback-Leibler (KL) divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the diffusion tensors being compared and this leads to a novel closed form expression for the "distance" as well as the mean value of a DTI. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in segmentation and many other applications. We then incorporate this new tensor "distance" in a region based active contour model for DTI segmentation. Synthetic and real data experiments are shown to depict the performance of the proposed model.}, address = {Siemens Corporate Research Inc, Princeton, NJ 08540, USA.}, author = {Wang, Z. and Vemuri, B. C.}, citeulike-article-id = {849762}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/16229414}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=16229414}, issn = {0278-0062}, journal = {IEEE Trans Med Imaging}, keywords = {dti, neuroscience, pubmed, segmentation}, month = {October}, number = {10}, pages = {1267--1277}, posted-at = {2006-09-19 15:10:45}, priority = {4}, title = {DTI segmentation using an information theoretic tensor dissimilarity measure.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16229414}, volume = {24}, year = {2005} }

TY - JOUR ID - citeulike:849762 L3 - citeulike-article-id:849762 N2 - In recent years, diffusion tensor imaging (DTI) has become a popular in vivo diagnostic imaging technique in Radiological sciences. In order for this imaging technique to be more effective, proper image analysis techniques suited for analyzing these high dimensional data need to be developed. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of DTI. In a DTI, the symmetric positive definite (SPD) diffusion tensor at each voxel can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the Kullback-Leibler (KL) divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the diffusion tensors being compared and this leads to a novel closed form expression for the "distance" as well as the mean value of a DTI. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in segmentation and many other applications. We then incorporate this new tensor "distance" in a region based active contour model for DTI segmentation. Synthetic and real data experiments are shown to depict the performance of the proposed model. IS - 10 JF - IEEE Trans Med Imaging SN - 0278-0062 AD - Siemens Corporate Research Inc, Princeton, NJ 08540, USA. EP - 1277 TI - DTI segmentation using an information theoretic tensor dissimilarity measure. VL - 24 SP - 1267 KW - dti KW - neuroscience KW - pubmed KW - segmentation AU - Wang, Z AU - Vemuri, BC PY - 2005/10// UR - http://view.ncbi.nlm.nih.gov/pubmed/16229414 ER -