TY - JOUR
ID - 7259
TI - Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation
JO - Iranian Journal of Medical Physics
JA - IJMP
LA - en
SN -
AU - Charmi, Mostafa
AU - Mahlooji Far, Ali
AD - PhD Candidate of Biomedical Engineering, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran,
AD - Associate Professor, Electrical and Computer Engineering Dept., Tarbiat Modares University, Tehran, Iran
Y1 - 2010
PY - 2010
VL - 7
IS - 2
SP - 21
EP - 39
KW - Biological Phantom
KW - Diffusion Tensor Images
KW - Log-Euclidean Metric
KW - Segmentation
DO - 10.22038/ijmp.2010.7259
N2 - 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.
UR - https://ijmp.mums.ac.ir/article_7259.html
L1 - https://ijmp.mums.ac.ir/article_7259_bce2f458f41f35f279506842f258086f.pdf
ER -