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Neural Computing and Applications

, Volume 30, Issue 7, pp 2029–2045 | Cite as

Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain

  • Haithem Hermessi
  • Olfa Mourali
  • Ezzeddine Zagrouba
S.I. : Deep Learning for Biomedical and Healthcare Applications
  • 267 Downloads

Abstract

Recently, deep learning has been shown effectiveness in multimodal image fusion. In this paper, we propose a fusion method for CT and MR medical images based on convolutional neural network (CNN) in the shearlet domain. We initialize the Siamese fully convolutional neural network with a pre-trained architecture learned from natural data; then, we train it with medical images in a transfer learning fashion. Training dataset is made of positive and negative patch pair of shearlet coefficients. Examples are fed in two-stream deep CNN to extract features maps; then, a similarity metric learning based on cross-correlation is performed aiming to learn mapping between features. The minimization of the logistic loss objective function is applied with stochastic gradient descent. Consequently, the fusion process flow starts by decomposing source CT and MR images by the non-subsampled shearlet transform into several subimages. High-frequency subbands are fused based on weighted normalized cross-correlation between feature maps given by the extraction part of the CNN, while low-frequency coefficients are combined using local energy. Training and test datasets include pairs of pre-registered CT and MRI taken from the Harvard Medical School database. Visual analysis and objective assessment proved that the proposed deep architecture provides state-of-the-art performance in terms of subjective and objective assessment. The potential of the proposed CNN for multi-focus image fusion is exhibited in the experiments.

Keywords

Convolutional neural networks Shearlet transform Multimodal medical image fusion Transfer learning Similarity metric learning 

Notes

Acknowledgements

The authors would like to thank Dr. Yu Liu, School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, China, for making his code and trained CNN model available online. Authors would also like to acknowledge the reviewers for their invaluable and constructive comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Intelligent Systems in Imaging and Artificial Vision (SIIVA), LIMTIC Laboratory, Higher Institute of Computer ScienceUniversity of Tunis El ManarArianaTunisia

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