Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain

被引：6

作者：

Li, Liangliang ^{[1
,2
]}

Si, Yujuan ^{[1
,2
]}

Wang, Linli ^{[3
]}

Jia, Zhenhong ^{[4
]}

Ma, Hongbing ^{[5
]}

机构：

[1] Jilin Univ, Zhuhai Coll, Sch Elect Informat Engn, Zhuhai 519041, Peoples R China

[2] Jilin Univ, Coll Commun Engn, Changchun 130012, Peoples R China

[3] Xinxiang Univ, Sch Math & Informat Sci, Xinxiang 453003, Henan, Peoples R China

[4] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China

[5] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

来源：

JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS | 2020年 / 10卷 / 08期

关键词：

Brain Image; Image Enhancement; NSST; SVD; CLAHE; BI-HISTOGRAM EQUALIZATION; CONTOURLET TRANSFORM; FUSION APPROACH; CONTRAST; ALGORITHM; BRIGHTNESS; NSCT;

D O I：

10.1166/jmihi.2020.3111

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.

引用

页码：1785 / 1794

页数：10

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