Multimodal Medical Image Fusion Based on Intuitionistic Fuzzy Sets

Multimodal medical image fusion is the process of combining two or more 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 lacks correlation. Intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian and clinical image processing as more uncertainties are obtained compared to fuzzy set theory. In this paper, an algorithm based on an interval-valued intuitionistic fuzzy set (IVIFS) is presented to efficiently fuse multimodal medical images and the final fused image is passed through a median filter to remove noise. Simulations on a few sets of multimodal medical images such as a fuzzy transformation and an intuitionist fuzzy collection were carried out and contrasted with the existing prevailing strategies. The prevalence of the proposed technique was introduced and supported. The resultant fused image superiority is checked with various quality estimations such as entropy, spatial frequency (SF), and average gradient (AG).