ASSESSMENT OF THE LOG-EUCLIDEAN METRIC PERFORMANCE IN DIFFUSION TENSOR IMAGE SEGMENTATION

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CHARMI MOSTAFA; MAHLOOJI FAR ALI

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Journal: IRANIAN JOURNAL OF MEDICAL PHYSICS Year:2010 | Volume:7 | Issue:2 (27) Page(s): 21-39

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Title

ASSESSMENT OF THE LOG-EUCLIDEAN METRIC PERFORMANCE IN DIFFUSION TENSOR IMAGE SEGMENTATION

Author(s)

CHARMI MOSTAFA | MAHLOOJI FAR ALI | Issue Writer Certificate

Keywords

BIOLOGICAL PHANTOMQ3

DIFFUSION TENSOR IMAGEQ3

LOG-EUCLIDEAN METRICQ3

SEGMENTATIONQ3

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 achicve 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.

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APA: Copy

CHARMI, MOSTAFA, & MAHLOOJI FAR, ALI. (2010). ASSESSMENT OF THE LOG-EUCLIDEAN METRIC PERFORMANCE IN DIFFUSION TENSOR IMAGE SEGMENTATION. IRANIAN JOURNAL OF MEDICAL PHYSICS, 7(2 (27)), 21-39. SID. https://sid.ir/paper/96925/en

Vancouver: Copy

CHARMI MOSTAFA, MAHLOOJI FAR ALI. ASSESSMENT OF THE LOG-EUCLIDEAN METRIC PERFORMANCE IN DIFFUSION TENSOR IMAGE SEGMENTATION. IRANIAN JOURNAL OF MEDICAL PHYSICS[Internet]. 2010;7(2 (27)):21-39. Available from: https://sid.ir/paper/96925/en

IEEE: Copy

MOSTAFA CHARMI, and ALI MAHLOOJI FAR, “ASSESSMENT OF THE LOG-EUCLIDEAN METRIC PERFORMANCE IN DIFFUSION TENSOR IMAGE SEGMENTATION,” IRANIAN JOURNAL OF MEDICAL PHYSICS, vol. 7, no. 2 (27), pp. 21–39, 2010, [Online]. Available: https://sid.ir/paper/96925/en

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