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Iranian Journal of Medical Physics، جلد ۷، شماره ۲، صفحات ۲۱-۳۹
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عنوان انگلیسی Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله مصطفی چرمی | mostafa charmi
phd candidate of biomedical engineering, department of electrical and computer engineering, tarbiat modares university, tehran, iran,

سازمان اصلی تایید شده: دانشگاه تربیت مدرس (Tarbiat modares university)

علی محلوجی فرد | ali mahlooji far
associate professor, electrical and computer engineering dept., tarbiat modares university, tehran, iran

سازمان اصلی تایید شده: دانشگاه تربیت مدرس (Tarbiat modares university)

نشانی اینترنتی http://ijmp.mums.ac.ir/article_7259.html
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Paper
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