Medical MRI Image Enhancement Based on Curvelet Transform and Fuzzy Algorithm

• Published in: 2020 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
• Language: English
• 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
• DOI: 10.1109/ITAIC49862.2020.9339186

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.