Noise reduction in brain magnetic resonance imaging using adaptive wavelet thresholding based on linear prediction factor.

Introduction: Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often suffer from limitations like edge and texture loss, poor smoothness, and the need for manual parameter tuning. Methods: This study introduces a novel adaptive wavelet thresholding technique for noise reduction in brain MRI. The proposed method utilizes a linear prediction factor to adjust the threshold adaptively. This factor leverages temporal information and features from both the original and noisy images to determine a weighted threshold. This dynamic thresholding approach aims to selectively reduce or eliminate noise coefficients while preserving essential image features. Results: The proposed method was rigorously evaluated against existing state-of-the-art noise reduction techniques. Experimental results demonstrate significant improvements in key performance metrics, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Discussion: The proposed adaptive thresholding technique effectively addresses the limitations of existing methods by providing a more efficient and accurate noise reduction approach. By dynamically adjusting the threshold based on image-specific characteristics, this method effectively preserves image details while effectively suppressing noise. These findings highlight the potential of the proposed method for enhancing the quality and interpretability of brain MRI images.