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