IJARCCE - Computer and Communication Engineering

Abstract

3 Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). In the proposed method noisy image is first decomposed into sub-band by wavelet transform and the nonlocal means filter is applied to each sub-band. This proposed method preserves the wavelet coefficients corresponding to the structures, while effectively suppressing noisy ones. Experimental results are also compared with the other different techniques like median, wiener, wavelet, wavelet based wiener, non-local mean. The quality of the output images is measured by the statistical quantity measures such as peak signal-to-noise ratio (PSNR), signal-to- noise ratio (SNR) and Mean square error (MSE) .The quantitative and the qualitative measures used as the quality metrics demonstrate the ability of the proposed method for noise suppression. Magnetic Resonance Images (MRI) are widely used for diagnosis and the treatment of brain tumors. It is the most powerful imaging technique developed to study the structural features and the functional characteristics of the internal body parts. It possesses good contrast resolution for different tissues and has advantages over computerized tomography (CT) for brain tissue studies. The diagnostic and visual quality of the MR images are affected by the noise added while acquisition. Noise in MRI (1) is mainly due to thermal noise that is induced by the movement of the charged particles in the radio frequency coils as well as the small anomalies in the preamplifiers. The presence of noise not only produces undesirable visual quality but also lowers the visibility of low contrast objects. Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. In recent years, wavelet transform shows a clear advantage in the field of signal and image denoising domains, and has many research results. The important property of a good image-denoising model is that it should completely remove noise as far as possible as well as preserve edges. In this paper effectiveness of six denoising algorithms viz. median filter (2),wiener filter(3),wavelet filter(4),wavelet based wiener(5),NLM(6),wavelet based NLM(7) using MRI images in the presence of additive white Gaussian noise is compared. Wavelet filter (4) removes noise pretty well in smooth regions but perform poorly along the edges. Image denoising has been extensively studied and thus there is a large amount of literature on denoising. Among these numerous works, we will briefly mention only a few of recently developed methods that are related with our method, specifically the wavelet domain coefficient thresholding and modeling (8),(9),(10) and nonlocal means filter (6).The problem with the conventional wavelet domain filtering is the removal of small but important coefficients while thresholding or the generation of unwanted coefficients in the probabilistic modeling approach as stated above. In this paper, it is expected that the nonlocal means filtering of the coefficients can alleviate these problems while effectively removing noisy coefficients. Specifically we propose a wavelet domain image denoising method where the nonlocal means filtering is applied to each of the subbands. By the nonlocal means filtering, the small wavelet coefficients which are part of important image structures are well kept while suppressing the noisy coefficients, whereas the conventional wavelet denoising methods sometimes suppress small but important coefficients as well.