Multimodal Medical Image Fusion Based on Interval-Valued Intuitionistic Fuzzy Sets

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.