University of Padova
Edge Hill University
Queensland University of Technology
University of Bedfordshire
Subjectspulse-coupled neural network
medical image fusion
non-subsampled shearlet transform
multimodal medical imaging
Subject Categories::G730 Neural Computing
MetadataShow full item record
AbstractIn 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.
CitationTan W, Tiwari P, Pandey HM, Moreira C, Jaiswal AK (2020) 'Multimodal medical image fusion algorithm in the era of big data', Neural Computing and Applications, (), pp.-.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF