An adaptive two-scale biomedical image fusion method with statistical comparisons. (November 2020) - Du, Jiao; Fang, Meie; Yu, Yufeng; Lu, Gang
Highlights: Adaptive two-scale decomposition is for pseudo-color biomedical image. Significance Friedman test is to judge noise metric VSNR . Bigger and smaller patch sizes are used to obtain detail grey image. The compared methods are at 2013–2019 in transform and spatial domain. Nemenyi test, median are adopted to evaluate significance of proposed method. Abstract: 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.