Enhanced JAYA optimization based medical image fusion in adaptive non subsampled shearlet transform domain

• DOI: 10.1016/j.jestch.2022.101245
• Publication Date: 2022-09-13T10:14:45Z
• Authors: Suresh Shilpa; M. Ragesh Rajan; C.S. Asha; Lal Shyam
• Keywords: Multi modal image fusion; Enhanced JAYA optimization; 0202 electrical engineering, electronic engineering, information engineering; ANSST; 02 engineering and technology; TA1-2040; Engineering (General). Civil engineering (General)

Multi-modal image fusion has gained popularity in the medical field as it assists doctors to view diverse modalities a single image. The treatment is effectively planned by looking into fused that helps diagnose diseases. aims merge texture features from multiple images proposed method includes application of Adaptive window-based Non-Subsampled Shearlet Transform (ANSST) on source separate low and high-frequency directional sub-bands. Further, an enhanced JAYA (EJAYA) optimization framework utilized obtain adaptive weights for combining sub-bands multi-modal fusion. low-frequency bands are using max rule based average energy entire process focuses preserving band's while improving details combined In end, inverse ANSST applied merged components get Extensive experiments conducted data sets obtained Brain Atlas website comprising more than 100 images. significance current approach validated qualitative quantitative assessments. exhibits good performance terms subjective analysis compared recent well-known techniques.