Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation | ||
| Iranian Journal of Medical Physics | ||
| مقاله 3، دوره 7، شماره 2، شهریور 2010، صفحه 21-39 اصل مقاله (1.09 M) | ||
| نوع مقاله: Original Paper | ||
| شناسه دیجیتال (DOI): 10.22038/ijmp.2010.7259 | ||
| نویسندگان | ||
| Mostafa Charmi1؛ Ali Mahlooji Far* 2 | ||
| 1PhD Candidate of Biomedical Engineering, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran, | ||
| 2Associate Professor, Electrical and Computer Engineering Dept., Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Biological Phantom؛ Diffusion Tensor Images؛ Log-Euclidean Metric؛ Segmentation | ||
|
آمار تعداد مشاهده مقاله: 841 تعداد دریافت فایل اصل مقاله: 1,837 |
||