Multimodal medical image fusion algorithm in the era of big data

• Published in: Neural computing & applications Vol. 37; no. 28; pp. 22995 - 23015
• Main Authors: Tan, Wei, Tiwari, Prayag, Pandey, Hari Mohan, Moreira, Catarina, Jaiswal, Amit Kumar
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer vision; Data Mining and Knowledge Discovery; Decomposition; Image acquisition; Image Processing and Computer Vision; Medical diagnosis; Medical imaging; Methods; Morphology; Neural networks; Probability and Statistics in Computer Science; S.I. : Data Fusion in the era of Data Science; Special Issue on Data Fusion in the era of Data Science; Tomography; Wavelet transforms; Medical image fusion; Non-subsampled shearlet transform; Pulse-coupled neural network; Multimodal medical imaging
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-020-05173-2

In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer’s, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.