Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

Abstract

Medical images have become omnipresent in diagnosis and therapy. However, they can be affected by various types of noise that reduce image quality and make the final diagnostic decision difficult. The main objective of this research is to effectively remove the noise while preserving the important image characteristics. This paper proposes a novel approach for image denoising based on discrete wavelet transform (DWT) with the selection of the best decomposition level and mother wavelet. Then, the thresholding function is carried out in the detail coefficients. Optimal thresholding is done using new optimization techniques such as the crow search algorithm and social spider optimization techniques. Finally, the inverse of DWT is applied to reconstruct the denoised image. The proposed method is evaluated using peak signal to noise ratio, mean square error, and the structural similarity index measure. The experimental results show the efficiency of the optimization-based denoising method over standard methods. Interesting results are obtained with all kinds of noise, and improvements about 30 dB can be reached with the Rician noise.

Enhancing and Denoising Mammographic Images for Tumor Detection using Bivariate Shrinkage and Modified Morphological Transforms

“…Wavelet analysis, valued for its time-frequency characteristics, finds significant use in image processing [13], particularly in denoising medical images like mammograms. Numerous research papers present this application [2][3][4][5][6][7][8]. The cornerstone of our denoising method is the use of the Stationary Wavelet Transform (SWT).…”

“…Fan et al (2019) [4] presented a noise removal technique for images in the wavelet domain that utilizes wavelet thresholding and Wiener filtering. Benhassine et al (2021) [5] proposed an optimal image denoising method used for medical images using discrete wavelet transform (DWT). The obtained coefficients are thresholded using optimization algorithms.…”

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

“…The optimal threshold t à 1 , t à 2 , …, t à K È É can be obtained by the maximum TS as shown in Equation (16).…”

“…Traditional image segmentation techniques are divided into the following categories: graph-based, [9][10][11] edge-based, 12,13 clustering-based, 14,15 thresholdingbased, 16,17 etc. Out of these techniques, thresholdingbased are easy operation and have outstanding performance, which is successfully implemented in various application areas.…”

Study on Mother Wavelet Optimization Framework Based on Changepoint Detection of Hydrological Time Series

“…Moradi et al (2022) optimized MWT by comparing the similarity of cross-correlation function, signal-to-noise ratio, and mean standard error between the denoised series and the original. Benhassine et al (2021) determined the optimal MWT by comparing the minimum mean square error between the original image and the denoised. Strömbergsson et al (2019) proposed and verified the validity of using the Shannon entropy of the wavelet coefficients as the index for selecting MWT.…”