PATCH-WISE IMAGE DE-NOISING TECHNIQUE FOR IMPROVED PMRI USING ITERATIVE BILATERAL FILTER, LRMD AND SVM OF MEDICAL IMAGES.

The most challenging research area in image processing is image denoising. This technique not only some self-possessed technical difficulties, but also may result in the demolition of the image (i.e. making it blur). There are various important component of large number of applications such as in medical diagnosis. In medical research there are different medical images like X- Ray, MRI,PET and CT gave minute to minute information about brain and whole body. The image denoising techniques includes parallel magnetic resonance imaging (pMRI) technique which can speed up MRI scan through a multi-channel coil array receiving signal simultaneously. Nevertheless, noise amplification and aliasing artifacts are serious in pMRI reconstructed images at high accelerations. An image enhancement method is proposed by using low rank matrix decomposition, LRMD and support vector machine, SVM. Low rank matrix decomposition is applied on image to remove the noises and enhancing the quality of an image. It describes the problem of finding and exploiting low-dimensional structures in high- dimensional data. The aim of Low Rank Matrix approximation based image enhancement is that it removes the various types of noises in the adulterate images simultaneously. The noise and aliasing artifacts are removed from the structured Matrix by applying sparse and low rank matrix decomposition method. The support vector machine exhibits video which is converted into different sizes of frames so that it can be enhanced easily. Then noisy image and enhanced image are compared to obtain higher signal to noise ratio and other parameters like Peak Signal to Noise Ratio PSNR, Structural Similarity Index Matrix SSIM and Mean Square Error MSE for qualitative assessment to the enhancement result. This method can effectively remove both noise and residual aliasing artifact from pMRI reconstructed noisy images, and produce higher peak signal noise rate (PSNR) and structural similarity index matrix (SSIM) than other state-of-the-art De-noising methods. Here we propose image de-noising using low rank matrix decomposition (LMRD) and Support vector machine (SVM). The proposed method gives more clear image with higher PSNR and improved SSIM value than the previous methods.