Multimodal Medical Image Fusion Based on Intuitionistic Fuzzy Sets and Weighted Activity Measure in NSST Domain

Background: In the extraction of information from multimodality images, anatomical and functional image fusion became an effective tool in the applications of clinical imaging. Objective: A new approach to fuse anatomical and functional images that use the combination of activity measure and intuitionistic fuzzy sets in the NSST domain is presented. Methods: First, the high and low-frequency sub-images of source images are obtained by utilizing NSST decomposition, which represents them in multi-scale and multi-directions. Next, the high-frequency sub-images are applied to intuitionistic fuzzy sets, in which the fused coefficients are selected using an activity measure called fuzzy entropy. Results: The multiplication of weighted local energy and weighted sum modified Laplacian is used as an activity measure to fuse the low-frequency sub-images. At last, the reconstruction of the final fused image is done by applying the inverse NSST on the above-fused coefficients. Conclusion: The efficacy of the proposed fuzzy-based method is verifiable by five different modalities of anatomical and functional images. Both subjective and objective calculations showed better results than existing methods.