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<p>Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.</p>
</abstract>

Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.

Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

PG and Research Department of Computer Science

Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically...

Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

Online veröffentlicht: 13. Sept. 2020

Seitenbereich: 130 - 146

Eingereicht: 16. März 2020

Akzeptiert: 22. Juli 2020

DOI: https://doi.org/10.2478/cait-2020-0033

Schlüsselwörter
Medical Image Denoising, Multiscale Transforms, Shrinkage Thresholding

© 2020 S. Shajun Nisha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

Online veröffentlicht: 13. Sept. 2020

Seitenbereich: 130 - 146

Eingereicht: 16. März 2020

Akzeptiert: 22. Juli 2020

DOI: https://doi.org/10.2478/cait-2020-0033

SchlüsselwörterMedical Image Denoising, Multiscale Transforms, Shrinkage Thresholding

© 2020 S. Shajun Nisha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Medical Image Denoising, Multiscale Transforms, Shrinkage Thresholding