%0 Journal Article
%T Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation
%J Iranian Journal of Medical Physics
%I Mashhad University of Medical Sciences
%Z 2345-3672
%A Charmi, Mostafa
%A Mahlooji Far, Ali
%D 2010
%\ 06/01/2010
%V 7
%N 2
%P 21-39
%! Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation
%K Biological Phantom
%K Diffusion Tensor Images
%K Log-Euclidean Metric
%K Segmentation
%R 10.22038/ijmp.2010.7259
%X Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI) segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this paper is to assess the possible substitution of the geodesic metric with the Log-Euclidean one to reduce the computational cost of a statistical surface evolution algorithm. Materials and Methods: We incorporated the Log-Euclidean metric in the statistical surface evolution algorithm framework. To achieve this goal, the statistics and gradients of diffusion tensor images were defined using the Log-Euclidean metric. Numerical implementation of the segmentation algorithm was performed in the MATLAB software using the finite difference techniques. Results: In the statistical surface evolution framework, the Log-Euclidean metric was able to discriminate the torus and helix patterns in synthesis datasets and rat spinal cords in biological phantom datasets from the background better than the Euclidean and J-divergence metrics. In addition, similar results were obtained with the geodesic metric. However, the main advantage of the Log-Euclidean metric over the geodesic metric was the dramatic reduction of computational cost of the segmentation algorithm, at least by 70 times. Discussion and Conclusion: The qualitative and quantitative results have shown that the Log-Euclidean metric is a good substitute for the geodesic metric when using a statistical surface evolution algorithm in DTIs segmentation.
%U http://ijmp.mums.ac.ir/article_7259_bce2f458f41f35f279506842f258086f.pdf