ArticleScientific reports2024
Enhanced multimodal medical image fusion via modified DWT with arithmetic optimization algorithm.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Performance Analysis of Discrete Wavelet Transform Bases for Multimodal Medical Image Decomposition and Fusion Quality Assessment.Journal of imaging · 2026Article
- NFSAOA: a new approach of medical brain image fusion using neutrosophic fuzzy set and arithmetic optimization algorithm.Medical & biological engineering & computing · 2026Article
- MSML-DenseXmer: harnessing vision transformers through integration with novel dense networks for medical image fusion.Scientific reports · 2026Article
- Volumetric photoacoustic/ultrasound image fusion and enhancement with dual-tree complex wavelet transform.Biomedical optics express · 2026Article
- Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.Scientific reports · 2025Article
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Abstract
Medical image fusion (MIF) techniques are proficient in combining medical images in distinct morphologies to obtain a reliable medical analysis. A single modality image could not offer adequate data for an accurate analysis. Therefore, a novel multimodal MIF-based artificial intelligence (AI) method has been presented. MIF approaches fuse multimodal medical images for exact and reliable medical recognition. Multimodal MIF improves diagnostic accuracy and clinical decision-making by combining complementary data in different imaging modalities. This article presents a new multimodal medical image fusion model utilizing Modified DWT with an Arithmetic Optimization Algorithm (MMIF-MDWTAOA) approach. The MMIF-MDWTAOA approach aims to generate a fused image with the significant details and features from each modality, leading to an elaborated depiction for precise interpretation by medical experts. The bilateral filtering (BF) approach is primarily employed for noise elimination. Next, the image decomposition process uses a modified discrete wavelet transform (MDWT) approach. However, the approximation coefficient of modality_1 and the detailed coefficient of modality_2 can be fused interchangeably. Furthermore, a fusion rule is derived from combining the multimodality data, and the AOA model is enforced to ensure the optimum selection of the fusion rule parameters. A sequence of simulations is accomplished to validate the enhanced output of the MMIF-MDWTAOA technique. The investigational validation of the MMIF-MDWTAOA technique showed the highest entropy values of 7.568 and 7.741 bits/pixel over other approaches.
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