Medical image denoising based on 2D discrete cosine transform via ant colony optimization

被引：19

作者：

Miri, Aref ^{[1
]}

Sharifian, Saeed ^{[2
]}

Rashidi, Shima ^{[2
]}

Ghods, Madjid ^{[3
]}

机构：

[1] Islamic Azad Univ, Cent Tehran Branch, Young Researchers & Elite Club, Tehran, Iran

[2] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran

[3] Khaje Nasir Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

OPTIK | 2018年 / 156卷

关键词：

Medical imaging; Gaussian noise; Two dimensional discrete cosine transform; Ant colony optimization; PSNR and SSIM; NOISE;

D O I：

10.1016/j.ijleo.2017.12.074

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In medical imaging, researchers usually encounter with different types of noise; for eliminating this noise, different methods have been suggested in both spatial and frequency domains. In this paper, a new method is proposed for removing Gaussian noise from medical images using two dimensional discrete cosine transform (2DDCT) and ant colony optimization (ACO) algorithm. In this algorithm, we attempted to identify the important frequency coefficients with the use of ant colony optimization and to eliminate the effects of noise by removing high frequency parts. Our proposed algorithm have been tested for various densities of the Gaussian noise and the experimental results show performance improvement in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM). (C) 2017 Elsevier GmbH. All rights reserved.

引用

页码：938 / 948

页数：11

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