Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non‐membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

The main objective of image fusion for multimodal medical images is to retrieve valuable information by combining multiple images obtained from various sources into a single image suitable for better diagnosis. In this paper, a detailed survey on various existing medical image fusion algorithms, with a comparative discussion is presented. Image fusion algorithms available in the current literature are categorized into various methods known as (1) morphological methods, (2) human value system operator based methods, (3) sub-band decomposition methods, (4) neural network based methods, and (5) fuzzy logic based methods. This research concludes that even though there exists a few open-ended creative and logical difficulties, the fusion of medical images in many combinations assists in utilizing medical image fusion for medicinal diagnostics and examination. There is tremendous progress in the fields of deep learning, artificial intelligence and bio-inspired optimization techniques. Effective utilization of these techniques can be used to further improve the efficiency of image fusion algorithms.

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Multimodal medical image fusion is the process of combining two multimodal medical images to increase the quality and to extract maximum information from the output image for better treatment and precise diagnosis. The fused image obtained from non-fuzzy sets lags with complementary information. Compared with fuzzy set theory, intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian and medical image processing as more uncertainties are measured. In this paper, an algorithm based on an interval-valued intuitionistic fuzzy set (IVIFS) is presented for efficiently fusing multimodal medical images and the final fused image is passed through a median filter to remove noise. Simulations on few sets of multimodal medical images are performed and compared with the existing fusion methods, such as an intuitionistic fuzzy set and fuzzy transform. The superiority of the proposed method is presented and is justified. Fused image quality is additionally checked with different quality measurements, for example, entropy, spatial frequency (SF), average gradient (AG), etc.