Optimized Bio-inspired Spiking Neural Models based Anatomical and Functional Neurological Image Fusion in NSST Domain

Abstract

Fusion of complementary anatomical and functional information present in multi-modal medical images provides improved visualization of various bodily structures and assists radiologist to infer more factual diagnostic interpretations. Inspired by the neuronal assemblies of mammal's visual cortex, spiking neural models such as dual-channel pulse coupled neural network (DCPCNN) and coupled neural P (CNP) system efficiently extract and integrate complementary information present in the source images. But, these models have various free parameters which are set using hit and trial approach in most of the conventional fusion methods. This paper presents an optimized multi-modal medical image fusion method in non-subsampled sheartlet transform (NSST) domain wherein the free parameters of both DCPCNN and CNP system are optimized using multi-objective grey wolf optimization (MOGWO). Extensive experiments are performed on various anatomical-functional images. Subjective and objective result analysis indicate that the proposed method effectively fuse important diagnostic information of the source images and also outperforms other state of the art fusion methods.