A novel multimodal medical image fusion model for Alzheimer's and glioma disease detection based on hybrid fusion strategies in non-subsampled shearlet transform domain

Background: Medical professionals may increase diagnostic accuracy using multimodal medical image fusion techniques to peer inside organs and tissues. Objective: This research work aims to propose a solution for diverse medical diagnostic challenges. Methods: We propose a dual-purpose model. Initially, we developed a pair of images using the intensity, hue, and saturation (IHS) approach. Next, we applied non-subsampled shearlet transform (NSST) decomposition to these images to obtain the low-frequency and high-frequency coefficients. We then enhanced the structure and background details of the low-frequency coefficients using a novel structure feature modification technique. For the high-frequency coefficients, we utilized the layer-weighted pulse coupled neural network fusion technique to acquire complementary pixel-level information. Finally, we employed reversed NSST and IHS to generate the fused resulting image. Results: The proposed approach has been verified on 1350 image sets from two different diseases, Alzheimer's and glioma, across numerous imaging modalities. Our proposed method beats existing cutting-edge models, as proven by both qualitative and quantitative evaluations, and provides valuable information for medical diagnosis. In the majority of cases, our proposed method performed well in terms of entropy, structure similarity index, standard deviation, average distance, and average pixel intensity due to the careful selection of unique fusion strategies in our model. However, in a few cases, NSSTSIPCA performs better than our proposed work in terms of intensity variations (mean absolute error and average distance). Conclusions: This research work utilized various fusion strategies in the NSST domain to efficiently enhance structural, anatomical, and spectral information.