Multimodal Medical Image Fusion Using Minimization Algorithm

Abstract

By merging anatomical and functional imaging, this multimodal image fusion technique aims to capture certain types of tissues and structures throughout the body. Anatomical imaging techniques can produce high-resolution images of interior organs. If we measure or find medical images independently, such as anatomical and functional imaging, we risk losing essential information. The proposed method or approach, unlike many current medical fusion methods, does not suffer from intensity attenuation or loss of critical information because both anatomical images and functional images combine relevant information from images acquired, resulting in both dark and light images being visible when combined. Colour mapping is conducted on functional and anatomical images, and the resulting images are deconstructed into coupled and independent components calculated using spare representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization issue is tackled using a different minimization algorithm, and the final fusion phase makes use of the max-absolute-value rule. The image is then normalized, and colour mapping is performed once again by colouring the layers until we obtain the perfect fusion image. This experiment makes use of a number of multimodal inputs, including the MR-CT method's competition when compared to existing approaches such as various medical picture fusion methods. For simulation purposes, the MATLAB R2017b version tool is used in this work.