Shearlet Transform-Based Novel Method for Multimodality Medical Image Fusion Using Deep Learning

Abstract

Multi-modality medical image fusion (MMIF) methods were widely used in a variety of clinical settings. For specialists, MMIF could provide an image containing anatomical and physiological information that can help develop diagnostic procedures. Different models linked to MMIF were proposed previously. However, there would be a need to enhance the functionality of prior methodologies. In this proposed model, a unique fusion model depending upon optimal thresholding and deep learning approaches are presented. An enhanced monarch butterfly optimization (EMBO) determines an optimal threshold with fusion rules as in shearlet transform. The efficiency of the fusion process mainly depends on the fusion rule and the optimization of the fusion rule can improve the efficiency of the fusion. The extraction element of the deep learning approach was then utilized to fuse high- and low-frequency sub-bands. The fusion technique was carried out using a convolutional neural network (CNN). The studies were carried out for MRI and CT images. The fusion results were attained and the proposed model was proved to offer effective performance with reduced values of error and improved values of correlation.