Multimodal Medical Image Fusion with Improved Multi-Objective Meta-Heuristic Algorithm with Fuzzy Entropy

Abstract

Medical image fusion enhances the significant and the valuable information such as exact abnormality localisation of the multimodal medical images. In the field of clinical environment, medical imaging acts as an important role in helping the doctors/radiologists. The information available in the images is important during the diagnosis. This can be enhanced using the multimodal medical image fusion technique through the integration of the information from several imaging modalities. Nowadays, several methodologies are proposed for fusing the medical images. Yet, the multimodal medical image fusion remains as a challenging task owing to the deprivation of medical images at the phase of acquisition. To handle this problem, this paper plans to develop the enhanced multi-objective medical image fusion model. Before initiating the fusion process, both the images to be fused are split into high-frequency sub-bands and low-frequency sub-bands by the improved Fast Discrete Curvelet Transform (FDCuT). Here, the fusion of low-frequency sub-images is accomplished by the averaging method, and high-frequency sub-images are fused by the optimised Type-2 fuzzy entropy. Both the FDCuT and Type-2 fuzzy entropy are enhanced by the multi-objective meta-heuristic algorithm by Adaptive Electric fish optimisation (A-EFO). The multi-objective function focuses on the “Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM), Feature SIMilarity (FSIM)”. The comparison of the developed methodology over the traditional approaches observes enhanced performance with respect to visual quality measures.