title: Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.
abstract: In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.
authors.affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Fadaee
authors.affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mousa
authors.initials: M
authors.lastname: Shamsi
authors.affiliation: Department of Electrical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Saberkari
authors.affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mohammad Hossein
authors.initials: MH
authors.lastname: Sedaaghi
keywords: Algorithms; Computed Tomography Images; Noise; Principal Component Analysis; Signal-to-Noise Ratio