Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks

TL;DR

This paper systematically evaluates wavelet-based denoising techniques for MRI brain images, identifying optimal configurations that improve noise reduction while preserving diagnostic features, thereby enhancing image quality for clinical use.

Contribution

It introduces a comprehensive analysis of wavelet parameters and thresholds, establishing optimal settings for medical image denoising that outperform existing methods.

Findings

Biorthogonal wavelet bior6.8 with universal thresholding is optimal.

Decomposition levels 2-3 yield best denoising results.

Significant noise reduction with preserved diagnostic details.

Abstract

Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, including salt-and-pepper distortions. This study investigates wavelet transform-based denoising methods for effective noise mitigation in medical images, with the primary objective of identifying optimal combinations of threshold values, decomposition levels, and wavelet types to achieve superior denoising performance and enhanced diagnostic accuracy. Through systematic evaluation across various noise conditions, the research demonstrates that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2-3 consistently achieves optimal denoising performance, providing significant noise reduction while preserving essential anatomical structures and diagnostic features critical for clinical applications.