@article {
author = {Charmi, Mostafa and Mahlooji Far, Ali},
title = {Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation},
journal = {Iranian Journal of Medical Physics},
volume = {7},
number = {2},
pages = {21-39},
year = {2010},
publisher = {Mashhad University of Medical Sciences},
issn = {2345-3672},
eissn = {2345-3672},
doi = {10.22038/ijmp.2010.7259},
abstract = {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.},
keywords = {Biological Phantom,Diffusion Tensor Images,Log-Euclidean Metric,Segmentation},
url = {https://ijmp.mums.ac.ir/article_7259.html},
eprint = {https://ijmp.mums.ac.ir/article_7259_bce2f458f41f35f279506842f258086f.pdf}
}