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White Matter Lesions Change Detection in MR Images Based on Fuzzy Nearness and Non-Subsampled Shear Waves

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A method based on fuzzy nearness and NonSubsampled shear waves (NSST) is proposed to solve the problem of change detection of white matter lesions region in MR images in this paper. First, the two images registered for detection are processed by Frost filter and a difference image is obtained based on the principle of fuzzy nearness. The coefficients that correspond to high frequency and low frequency are obtained by computing the NSST of the difference image. Finally, the change class and non-change class are divided using the FCM clustering algorithm on the image after performing Non-Subsampled inverse transform to the low-frequency and the high-frequency coefficients. Experimental results on MR images show that the proposed algorithm accurately detects changes in the change regions of white matter lesions, which implies that the automatic and quick detection of lesion change area is realized. It has a certain application value for clinical diagnosis of this particular medical question.
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Keywords: CHANGE DETECTION; FUZZY NEARNESS; NON-SUBSAMPLED SHEAR WAVES; WHITE MATTER LESIONS

Document Type: Research Article

Publication date: 2014-12-01

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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