Medical MRI Image Enhancement Based on Curvelet Transform and Fuzzy Algorithm

This paper proposes a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm. First, the MRI image is subjected to curvelet positive transform to obtain the curvelet coefficients at various scales and directions, and then the Monte-Carlo test method is used to estimate...

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Bibliographic Details
Published in2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) Vol. 9; pp. 202 - 206
Main Authors Hu, Qing, Min, Xinyi, He, Wen
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.12.2020
Subjects
Biomedical imaging
curvelet transform
enhancement
fuzzy algorithm
Image edge detection
Image enhancement
Magnetic resonance imaging
Monte Carlo methods
MRI image
Transforms
Visual effects
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DOI10.1109/ITAIC49862.2020.9339186

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Summary:This paper proposes a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm. First, the MRI image is subjected to curvelet positive transform to obtain the curvelet coefficients at various scales and directions, and then the Monte-Carlo test method is used to estimate each scale noise variance, and then apply hard threshold shrinkage processing to the curvelet coefficients. Finally, the Pal-King algorithm with modified membership function is used to perform fuzzy enhancement on the image after inverse curvelet transformation to obtain the final result image. We selected a brain MRI image to test the algorithm, the experimental results show that compared with the other two enhancement algorithms, the algorithm in this paper has higher PSNR and CONTRAST, which can effectively suppress noise, enhance the edges and details of the image, and has better visual effects.
DOI:10.1109/ITAIC49862.2020.9339186