view:45871 Last Update: 2024916
Mostafa Charmi, Ali Mahlooji Far
Assessment of the LogEuclidean Metric Performance in Diffusion Tensor Image Segmentation
ارزیابی عملکرد متریک لگاریتمیاقلیدسی در ناحیه بندی تصاویر تانسور انتشار

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 highquality 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 LogEuclidean one to reduce the computational cost of a statistical surface evolution algorithm. Materials and Methods: We incorporated the LogEuclidean metric in the statistical surface evolution algorithm framework. To achieve this goal, the statistics and gradients of diffusion tensor images were defined using the LogEuclidean 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 LogEuclidean 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 Jdivergence metrics. In addition, similar results were obtained with the geodesic metric. However, the main advantage of the LogEuclidean 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 LogEuclidean metric is a good substitute for the geodesic metric when using a statistical surface evolution algorithm in DTIs segmentation. 