An Improved Pre-processing Method for High-Quality MRI Images in Brain Tumor Detection
TL;DR
This study critically compares some noise removal methods, such as Gaussian, median, Wiener, and guided filtering, as well as Discrete Wavelet Transform (DWT)-based de-noising with soft thresholding with the proposed WNM hybrid model, which demonstrates the highest reconstruction quality at 5% noise.
Abstract
Magnetic Resonance Imaging (MRI) is among the effective methodologies to identify tumors in the brain, but this method may not be very reliable because of the challenges in acquiring the images, which may include image noise, contrast differences, and spatial variation of intensity. To solve these problems, this study suggests an innovative pre-processing framework that will be used to improve the quality of MRI images prior to segmentation and tumor analysis. This study critically compares some noise removal methods, such as Gaussian, median, Wiener, and guided filtering, as well as Discrete Wavelet Transform (DWT)-based de-noising with soft thresholding. The new hybrid model, based on the combination of the advantages of various methods, is a Wavelet-NLM-Median (WNM). WNM integrates multiresolution wavelet shrinkage, non-local redundancy modelling, and median-based edge preservation to achieve improved noise reduction while maintaining structural details. Extensive testing is performed across noise levels ranging from 5% to 50%, and performance is assessed using standard evaluation metrics such as PSNR, MSE, SSIM, and SNR. The proposed WNM hybrid model demonstrates the highest reconstruction quality at 5% noise, with a PSNR of 45.98 dB, MSE of 3.35, SSIM of 0.985, and SNR of 44.89 dB. These statistics were substantially better than those of the Wiener and Guided filters, as well as soft-thresholding based on DWT. Visual assessments also showed that the WNM hybrid approach does a better job of keeping tumor boundaries, fine textures, and structural patterns than any other baseline filtering method. This shows that it is better at restoring high-quality MRI images for later study. The improved MRI inputs used to pre-process training images for a deep learning segmentation model improve the accuracy of the segmentation and the sharpness of the boundaries in a way that could be quantified. The suggested WNM pipeline is quick, works with many types of modalities, and is simple to connect to clinical CAD systems. It is a giant leap in pre-processing the MRI to detect malignancies in the brain.