English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Shearlet transform has been widely applied in related fields due to its admirable properties in image approximation. Image fusion method based on accelerated non-negative matrix factorization (ANMF) and expanded energy of Laplace (EEOL) rules was proposed in this study to integrate the complementary information of medical images with multiple modalities and improve the accuracy of clinical diagnosis and therapy. First, the registered medical images were decomposed into lowand high-frequency sub-band coefficients in shearlet domain. Then, the ANMF rule was used in merging low-frequency coefficients. Next, the visual-contrast-based EEOL rule was adopted in extracting details of source images from high-frequency coefficients. Finally, the ultimate fused image was reconstructed by applying inverse shearlet transform. Experimental results reveal that aside from visual effect, the proposed method achieves the best in three of five criteria and the run time is reduced by 29.21% compared with a method based on non-subsampled contourlet transform (NSCT) in computed tomography (CT)–magnetic resonance imaging (MRI) fusion. Moreover, the proposed method takes the first place in four of five criteria with run time reduced by 48.32% and 24.55% compared with two shearlet-based methods in a MRI–positron emission tomography (PET) case. This study indicates that the proposed method is superior to the selected approaches in visual and statistical evaluation, which is conducive to clinical practice of medical image fusion.

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain