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Multimodal medical image fusion algorithm in the era of big data

  • Wei Tan
    ,
  • Prayag Tiwari
    ,
  • Hari Mohan Pandey
    ,
  • Catarina Moreira
    ,
  • Amit Kumar Jaiswal
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 22995-23015

Journal (Volume, Issue Number)

Neural Computing and Applications (Volume 37)

Publication milestones

  • Accepted/In press - 27/06/2020
  • Published - 08/07/2020

Publication status

Published - 08/07/2020

ISSN

0941-0643

External Publication IDs

  • handle.net: 10547/625659
  • Scopus: 85087621385

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