Medical Image Fusion Based On Feature Extraction And Sparse Representation

Abstract

Sparse representation has numerous benefits over traditional picture representation approaches as a novel multiscale geometric analysis technique. The normal sparse representation, on the other hand, ignores inherent structure and time complexity. A new fusion mechanism for multimodal medical images focused on sparse representation and judgment is presented in this article.A map is planned to address both of these issues at the same time. To allow the effects reserve more energy and edge knowledge, three decision maps are designed: structure information map (SM), energy information map (EM), and structure and energy map (SEM). The Laplacian of a Gaussian (LOG) captures the local structure function in SM, and the mean square variance detects the energy and energy distribution feature in EM. To increase the pace of the algorithm, the decision map is applied to the standard sparse representation dependent procedure. By improving the contrast and reserving more structure and energy details from the source pictures, the proposed solution also enhances the accuracy of the fused data. The findings of 36 classes of CT/MR, MR-T1/MR-T2, and CT/PET photos show that the SR and SEM-based approach outperforms five state-of-the-art approaches.