¹JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China, 215104
²The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou, China, 215006

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Abstract—Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy theory. Firstly, the image was decomposed by digital ridgelet transform to obtain the approximation coefficients and detailed coefficients in different sub-bands with directional parameters. Then the dynamic fuzzy theory was applied to construct a membership function to calculate coefficients from each sub-bands respectively, and a weight of sub-bands degree was adjust by precision requirement. At last similarity degrees are calculated by coefficients degree and weight. Medical images were classified by the result sort order of the degrees effectively.

Index Terms—medical image classification; ridgelet transform; dynamic fuzzy; similarity degree.

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Cite: Zhiming Cui, Guangming Zhang, "A Novel Medical Image Dynamic Fuzzy Classification Model Based on Ridgelet Transform," Journal of Software vol. 5, no. 5, pp. 458-465, 2010.