Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and Non-subsampled Contourlet Transforms

Abstract

Digital images are extensively used by the medical doctors during different stages of disease diagnosis and treatment process. In the medical field, noise occurs in an image during two phases: acquisition and transmission. During the acquisition phase, noise is induced into an image, due to manufacturing defects, improper functioning of internal components, minute component failures and manual handling errors of the electronic scanning devices such as PECT/SPECT, MRI/CT scanners. Nowadays, healthcare organizations are beginning to consider cloud computing solutions for managing and sharing huge volume of medical data. This leads to the possibility of transmitting different types of medical data including CT, MR images, patient details and much more information through internet. Due to the presence of noise in the transmission channel, some unwanted signals are added to the transmitted medical data. Image denoising algorithms are employed to reduce the unwanted modifications of the pixels in an image. In this paper, the performance of denoising methods with two dimensional transformations of nonsubsampled contourlets (NSCT), curvelets, double density dual tree complex wavelets (DD-DTCWT) are compared and analysed using the image quality measures such as peak signal to noise ratio, root mean square error, structural similarity index. In this paper, 200 MR images of brain (3T MRI scan), heart and breast are selected for testing the noise reduction techniques with above transformations. The results shows that the NSCT gives good PSNR values for random and impulse noises. DD-DTCWT has good noise suppressing capability for speckle and Rician noises. Both NSCT and DD-DTCWT copes well in images affected by poisson noises. The best PSNR value obtained for salt and pepper and additive white Guassian noises are 21.29 and 56.45 respectively. For speckle noises, DD-DTCWT gives 33.46 and it is better than NSCT and curvelet. The values 33.50 and 33.56 are the top PSNRs of NSCT and DD-DTCWT for poisson noises.