A Lightweight Multimode Medical Image Fusion Method Using Similarity Measure Between Intuitionistic Fuzzy Sets Joint Laplacian Pyramid

Abstract

Medical image fusion combines the multiple features of human tissue from different source images, which will be conducive to clinical diagnosis. Because of the human visual perception and mechanisms of medical imaging, abundant fuzzy features exist in medical image fusion with remarkable influence. To address this issue, we introduce the similarity measure of fuzzy set theory into medical image fusion to abstract and measure these fuzzy features. In this study, we present a new similarity measure of intuitionistic fuzzy set theory to represent image features and present a lightweight medical image fusion technique on the basis of this new measure. First, the new similarity measure is proposed according to geometric modeling technique and verified by mathematical justification. Second, a set of high frequency sub-images and a low frequency sub-image are produced by Laplacian pyramid decomposition to separate different image features. Third, our proposed similarity measure is utilized to describe and extract the detailed image features. Finally, two fusion rules and inverse Laplacian pyramid decomposition are sequentially used to get the fused image. This research shows that the similarity measure of fuzzy set theory can get excellent performance in medical image fusion. Experiments reveal that our similarity measure and medical image fusion method both have the superior performance to most of the existing methods.