Medical Image Denoising using Adaptive Threshold Based on Contourlet Transform

TL;DR

This paper introduces a new MRI image denoising method using contourlet transform, which outperforms wavelet-based methods in PSNR when removing noise from medical images.

Contribution

The paper presents an adaptive thresholding algorithm based on contourlet transform specifically designed for medical image denoising, demonstrating improved performance over existing wavelet methods.

Findings

Higher PSNR achieved compared to wavelet-based denoising.

Effective noise removal in MRI images with additive white Gaussian noise.

Demonstrated superiority in preserving image quality during denoising.

Abstract

Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform. Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio (PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.