Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images

An Improved Multimodal Medical Image Fusion Approach Using Intuitionistic Fuzzy Set and Intuitionistic Fuzzy Cross-Correlation

Multimodal medical image fusion (MMIF) is the process of merging different modalities of medical images into a single output image (fused image) with a significant quantity of information to improve clinical applicability. It enables a better diagnosis and makes the diagnostic process easier. In medical image fusion (MIF), an intuitionistic fuzzy set (IFS) plays a role in enhancing the quality of the image, which is useful for medical diagnosis. In this article, a new approach to intuitionistic fuzzy set-based MMIF has been proposed. Initially, the input medical images are fuzzified and then create intuitionistic fuzzy images (IFIs). Intuitionistic fuzzy entropy plays a major role in calculating the optimal value for three degrees, namely, membership, non-membership, and hesitation. After that, the IFIs are decomposed into small blocks and then perform the fusion rule. Finally, the enhanced fused image can be obtained by the defuzzification process. The proposed method is tested on various medical image datasets in terms of subjective and objective analysis. The proposed algorithm provides a better-quality fused image and is superior to other existing methods such as PCA, DWTPCA, contourlet transform (CONT), DWT with fuzzy logic, Sugeno's intuitionistic fuzzy set, Chaira's intuitionistic fuzzy set, and PC-NSCT. The assessment of the fused image is evaluated with various performance metrics such as average pixel intensity (API), standard deviation (SD), average gradient (AG), spatial frequency (SF), modified spatial frequency (MSF), cross-correlation (CC), mutual information (MI), and fusion symmetry (FS).