MR Image Enhancement using Adaptive Weighted Mean Filtering and Homomorphic Filtering

Abstract

Magnetic resonance image enhancement plays crucial role in numerous bio-medical applications. In this paper, the noisy magnetic resonance (MR) brain images were enhanced using Adaptive Weighted Mean Filtering (AWMF) and homomorphic filtering. The MR images always suffer from low contrast. Homomorphic filtering is popular technique to enhance the image contrast. Homomorphic filtering works based on illumination-reflectance model. It improves the image quality by doing contrast enhancement and dynamic range compression simultaneously. In general, MR images are affected by Rician noise, salt and pepper noise and Gaussian noise. Salt and pepper noise (SPN) considerably reduce the quality of the MR images. Contrast ratio and image quality is significantly degraded in the presence of SPN. Pre-processing is required for noisy MR images before applying to homomorphic filter. Many techniques have been proposed to de-noise the salt and pepper noise such as mean, median and adaptive filters. These filters are used to eliminate low level of SPN. High level of SPN can be eliminated by AWMF. In pre-processing, the AWMF is used to denoising the noisy images. Then de-noised image is enhanced using homomorphic filter. The efficiency of the proposed method is compared with median filter (MF) and based on pixel density filter (BPDF). The simulation results show that our proposed algorithm is more efficient than existing algorithms.