Multimodal medical image fusion using non-subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient

This research proposes a novel fusion scheme for non-subsampled shearlet transform (NSST) which is based on simplified model of pulse coupled neural network (PCNN). The images to be fused are acquired from Postgraduate Institute of Medical Education and Research, Chandigarh, India, and internet repository. The image database contains computed tomography and T2-weighted magnetic resonance images. The images to be fused are decomposed into approximation and detail sub-bands using NSST. The regional energy-based activity measure with consistency verification is applied to fuse the approximation sub-band of NSST. The novel morphological gradient of detail sub-bands is fed as external stimulus to PCNN to fuse detail sub-bands. The proposed method is compared with five state-of-the-art fusion schemes visually and using five fusion performance parameters. It is observed that the resultant images of the proposed fusion scheme show appropriate fusion characteristics and retain the bone, CSF and edema details in the clinical format required for disease evaluation by the radiologists. The proposed scheme requires lesser computational time than other state-of-the-art PCNN-based fusion schemes.