Абстрактный

Comparative Analysis of Methods to Denoise CT Scan Images

Tarandeep Chhabra , Geetika Dua, Tripti Malhotra

Medical images are generally noisy due to the physical mechanisms of the acquisition process. In CT Scan there is a scope to adapt patient image quality and dose. Reduction in radiation dose (i.e the amount of X-rays) affects the quality of image and is responsible for image noise in CT. Most of the denoising algorithms assume additive white Gaussian noise but however most medical images may contain non Gaussian noise like poisson noise in CT. This paper contains the comparative analysis of a number of denoising algorithms namely wiener filtering, wavelet decomposition, anisotropic diffusion, anisotropic diffusion in wavelet domain, wave atom decomposition, median filtering and NL-means filtering. Then, some quantitative performance metrics like PSNR, SNR, MSE, S/MSE and MAD are computed. This comparison helps in the assessment of image quality and fidelity. We conclude that the anisotropic diffusion in wavelet domain is the most efficient method in removing poisson noise from CT Scan images.