An adaptive two-scale biomedical image fusion method with statistical comparisons

被引:20
作者
Du, Jiao [1 ]
Fang, Meie [1 ]
Yu, Yufeng [2 ]
Lu, Gang [3 ]
机构
[1] Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China
[2] Guangzhou Univ, Dept Stat, Guangzhou 510006, Peoples R China
[3] Southeast Univ, Lab Image Sci & Technol, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China
基金
中国国家自然科学基金;
关键词
Base and detail; Friedman test; Otsu's method; Adaptive two-scale representation; Statistical significant analysis; QUALITY ASSESSMENT; MODEL; TRANSFORM; MRI; PET; DECOMPOSITION; INFORMATION;
D O I
10.1016/j.cmpb.2020.105603
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Two-scale image representation of base and detail in the spatial-domain is a well-known decomposition scheme for its lower computational complexity than that performed in the transform-domain in the field of image fusion. Unfortunately, for a pseudo-colour input image, the base and detail images in the spatial-domain obtained via image decomposition scheme always display in greyscale. In this paper, a two-scale image fusion method with adaptive threshold obtained by Otsu's method is proposed for pseudo-colour image in the colour space domain. For greyscale image, detail and base image are obtained using structural information extracted from the difference image between a global and a local patch size. Consequently, local edge-preserving filter for preserving luminance information and local energy with the discussed window size are adopted to combine base and detail image. Experimental results show that structural and luminance information has been better preserved in terms of subjective and objective evaluations for medical image and protein image fusion. Specially, a two-step non-parametric statistical test (Friedman test and Nemenyi post-hoc test) with p-values is adopted to analysis the statistical significant of the relative difference between the proposed and compared methods in terms of values of objective metrics including 30 co-registered pairs of imaging data. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
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