Multimodal Neurological Image Fusion Based On Adaptive Biological Inspired Neural Model In Nonsubsampled Shearlet Domain

Multimodal medical image fusion (MMIF) has a significant role for a better visualization of the diagnostic statistics, those help the medical professionals in the precise diagnosis of several critical diseases. This paper presents an improved fusion framework that uses the entire features extracted by the nonsubsampled shearlet transform (NSST) and adaptive biologically inspired neural model. The proposed scheme retains the required information without losing the resolution of the disease morphology. In the proposed method, the adaptive neural model based on local visibility and log Gabor energy based rules are applied to low and high-frequency components, respectively. The better fusion results obtained by the proposed approach are confirmed by a large extent of simulations on the different MR-SPECT and CT-MR neurological images. Based on all these simulated results, it states that the proposed approach is superior than the other approaches as it produces better visually fused images with improved computational measures.