Blind noise estimation-based CT image denoising in tetrolet domain. (7th February 2020)

• Record Type: Journal Article
• Authors: Diwakar, Manoj; Kumar, Pardeep
• Is Part Of: International journal of information and computer security. Volume 12:Number 2/3(2020)
• Page Start: 234
• Page End: 252
• Publication Date: 2020-02-07
• Subjects: tetrolet transform -- non-local means approach -- image denoising -- computed tomography; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1744-1765

Abstract

Recently in medical imaging, various cases of cancers have been explored because of high dose radiation in computed tomography (CT) scan examinations. These high radiation doses are given to patients to achieve good quality CT images. Instead of increasing radiation dose, an alternate method is required to get high quality images for diagnosis purpose. In this paper, we propose a method where, the noise of CT images will be estimated using patch-based gradient approximation. Further, estimated noise is used to denoise the CT images in tetrolet domain. In proposed scheme, a locally adaptive-based thresholding in tetrolet domain and non-local means filtering have been performed to suppress noise from CT images. Estimation noise from proposed method has been compared from added noise in CT images and it was observed that noise is almost correctly estimated by proposed method. To verify the strength of noise suppression in proposed scheme, comparison with recent other existing methods have been performed. The PSNR and visual quality of experimental results indicate that the proposed scheme gives excellent outcomes in compare to existing schemes.