Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model

Motivation: Medical image fusion has developed into an important technology, which can effectively merge the significant information of multiple source images into one image. Fused images with abundant and complementary information are desirable, which contributes to clinical diagnosis and surgical planning. Results: In this article, the concept of the skewness of pixel intensity (SPI) and a novel adaptive co-occurrence filter (ACOF)-based image decomposition optimization model are proposed to improve the quality of fused images. Experimental results demonstrate that the proposed method outperforms 22 state-of-the-art medical image fusion methods in terms of five objective indices and subjective evaluation, and it has higher computational efficiency.

Automated summary

In this article, a novel medical image fusion method is proposed, utilizing skewness of pixel intensity and an adaptive co-occurrence filter for image decomposition optimization. Experimental results show that this method outperforms 22 state-of-the-art methods in terms of quality and computational efficiency.