Medical image fusion using segment graph filter and sparse representation

This study proposes a novel medical image fusion approach based on the segment graph filter (SGF) and sparse representation (SR). Specifically, using the SGF, source images are decomposed into base and detail images, based on which the edge information is integrated into the fused image as much as possible. The base images are then fused applying a fusion rule based on the normalized Shannon entropy, whereas the detail images are fused using an SR-based fusion method. Finally, the resultant fused image is computed by combining the fused base and detail images. For quantitative performance evaluations, five metrics are adopted: the feature-based metric, structure-based metric, normalized mutual information, nonlinear correlation information entropy, and phase congruency metric. Experimental results indicate that the fusion performance of the proposed method is comparable to those of state-of-the-art methods with respect to both subjective visual performance and objective quantification.

Automated summary

The study proposes a novel medical image fusion approach based on segment graph filter and sparse representation, which achieves comparable fusion performance to state-of-the-art methods in terms of both subjective visual performance and objective quantification.