Journal
BIOINFORMATICS
Volume 38, Issue 3, Pages 818-826Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab721
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Funding
- National Natural Science Foundation of China [61801190]
- National Key Research and Development Project of China [2019YFC0409105]
- 'Thirteenth Five-Year Plan' Scientific Research Planning Project of Education Department of Jilin Province [JJKH20200997KJ, JJKH20200678KJ]
- Fundamental Research Funds for the Central Universities
- Graduate Innovation Fund of Jilin University
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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.
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.
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