Multimodal Medical Image Fusion Based on Fuzzy Sets with Orthogonal Teaching–Learning-Based Optimization

The purpose of an image fusion for medical images is to associate a number of images gained from many bases to a solitary image appropriate for better analysis. The vast majority of the best in class image fusing systems are based on non-fuzzy sets, and the fused image so obtained lags with complementary information. Fuzzy sets are strong-minded to be more appropriate for medical image processing as more hesitations are considered compared with non-fuzzy sets. In this paper, a procedure for efficiently fusing multimodal medical images is presented. In the proposed method, images are initially converted into intuitionistic fuzzy images (IFIs), and another target work called intuitionistic fuzzy entropy (IFE) is utilized for membership and non-membership capacities to accomplish the finest estimation of the bound. Next, the IFIs are compared using the fitness function, entropy. Then, orthogonal teaching–learning-based optimization (OTLBO) is introduced to optimize combination factors that change under teaching phase, and learner phase of OTLBO. Finally, the fused image is achieved using optimal coefficients. Simulations on several pairs of multimodal medical images are performed and matched with the current fusion approaches. The dominance of the proposed technique is presented and justified. Fused image quality is also verified with various quality metrics, such as peak signal-to-noise ratio (PSNR), universal quality index (UQI), structural similarity (SSIM), correlation coefficient (CC), entropy (E), spatial frequency (SF), edge information preservation (QAB/F), and standard deviation (SD).