Medical MRI Image Enhancement Based on Curvelet Transform and Fuzzy Algorithm

TLDR: Wang et al. as discussed by the authors proposed a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm, which can effectively suppress noise, enhance the edges and details of the image, and has better visual effects.

Abstract: 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.