Medical image denoising by parallel non-local means

被引：25
|
作者：
Xu Mingliang [1 ,2 ]
Lv Pei [1 ,2 ]
Li Mingyuan [1 ]
Fang Hao [1 ]
Zhao Hongling [2 ]
Zhou Bing [1 ,2 ]
Lin Yusong [2 ]
Zhou Liwei [3 ]
机构：
[1] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China
[2] Zhengzhou Univ, Cooperat Innovat Ctr Internet Healthcare, Zhengzhou 450000, Peoples R China
[3] Zhengzhou Univ, Affiliated Hosp 5, Zhengzhou 450052, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 195卷
基金：
中国国家自然科学基金;
关键词：
Medical image; Non-local Means; Denoising; Parallel algorithm; WAVELET; ALGORITHM; SIGNAL; SCALE;
D O I：
10.1016/j.neucom.2015.08.117
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The generation process of medical image will inevitably introduce certain noises. These noises will degrade the image quality and affect the final clinical diagnosis. Therefore, denoising plays an important role in the pre-processing of medical image before the formal diagnosis and treatment. In this paper, the classical NLM algorithm is improved to denoise medical images by involving a novel noise weighting function and parallelizing. In our experiment, plenty of medical images have been tested and experiment results show that our algorithm can achieve better results and higher efficiency compared with the original NLM method. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：117 / 122
页数：6
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