CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain

Abstract

In today era, computed tomography (CT) is one of the exceptionally proficient crucial devices in medical science for the clinical reason. The consistent improvement and broad utilization of computed tomography in medical science has uplifted the harmfulness of higher dose to the patient. Low radiation dose may prompt expanded noise and artifacts, which can influence the radiologists’ judgment. Therefore, we propose a method based on new shrinkage function in the nonsubsampled shearlet domain (NSST). In the proposed algorithm, method noise on multivariate shrinkage model is utilized viably by using stein's unbiased risk estimate and linear expansion of thresholds (SURE-LET) concept. To verify the execution of the proposed method, the qualitative and quantitative evaluations are performed. The results are evaluated over the both real noisy CT image and by adding Gaussian noise in real CT image and as well as on low complexity zoomed objects of noisy CT images. The results are also tested by some standard execution measurements, for example, PSNR, SSIM, ED, and DIV. The experimental results confirmed that proposed method is giving improved results in most cases.