Medical image De-noising schemes using wavelet transform with fixed form thresholding

Abstract

Medical Imaging is currently a hot area of biomedical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and transmission process. This results in blurry image visible in low contrast. The Image De-noising System (IDs) is used as a tool for removing image noise and preserving important data. Image de-noising is one of the most interesting research areas among researchers of technology-giants and academic institutions. For Criminal Identification Systems (CIS) & Magnetic Resonance Imaging (MRI), IDs is more beneficial in the field of medical imaging. This paper proposes algorithm for de-noising medical images using different types of wavelet transform, such as Haar, Daubechies, Symlets and Biorthogonal. In this paper noise image quality has been evaluated using filter assessment parameters like Variance, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It has been observed form the numerical results that, the performance of proposed algorithm reduced the mean square error and achieved best value of peak signal to noise ratio (PSNR). In this paper, the wavelet based de-noising algorithm has been investigated on medical images along with threshold.