Medical Image Denoising using Unit Linking PCNN Model

Image Denoising is tranquil challenges for many researchers. Image Denoising has a main aim to retain the important details and dislodge the noises as far as possible. Noise is the error in the image, its displays the disparate intensity values in lieu of true intensity values. It is an usual, the medical images contain some noises during some unavoidable reasons. Low contrast and poor quality are the major problems in medical images. In this work Gaussian noises and Speckle noises are added to MRI and X-Ray images. Contourlet transform and Unit Linking PCNN model is a best transformation function and it is used for decomposition. Contourlet is used to sustain the edges and contour regions. Unit Linking PCNN model is used to lessen the intricacy for picking the parameters. To diminish the noises some threshold function are used such as Bayes shrink,Block shrink and Neigh shrink. Performance of denoised image are appraised by Image Quality Index(IQI), Structural Similarity Index(SSIM), Normalized Cross Correlation(NCC), Peak Signal to Noise Ratio(PSNR).