Efficient MRI image enhancement by improved denoising techniques for better skull stripping using attention module-based convolution neural network

Abstract

Anatomical structure preservation throughout the denoising process is a challenge in the domain of medical imaging. The Rician noise introduced through the acquisition procedure by the Magnetic Resonance Imaging (MRI) scanner distorts the images. In this study, denoising using Wavelet-based Non-Local Median Filter (WBNLMF) and a novel contrast-enhancement method termed Improved Minimum Intensity Error Intuitionistic Fuzzy Contrast Enhancement (IMIEIFCET) is suggested. This methodology gives superior results while maintaining the edges and the brightness of the original image. An Attention Module-based Convolution Neural Network (AM-CNN) is suggested in the research as a methodology for skull stripping from MRI data. With a mean Dice coefficient of 0.998, a Sensitivity of 0.9975, and a Specificity of 0.9985, the proposed network exhibits result that are comparable to those of the specified Deep Learning (DL)-based technique.

Keywords

• Rician noise
• contrast enhancement
• MRI
• denoising
• skull stripping