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Multimodal Image Fusion Based on Non-subsampled Shearlet Transform and Neuro-Fuzzy

  • Haithem HermessiEmail author
  • Olfa Mourali
  • Ezzeddine Zagrouba
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 684)

Abstract

Due to the appealing advantages in term of medical decision making, the problem of multimodal medical image fusion has received focused research over the recent years. Moreover, complimentary imaging modalities such as CT and MRI are able to improve medical reliability by reducing uncertainty. In this paper, we propose a new algorithm for multimodal medical image fusion based on non-subsampled shearlet transform (NSST) and neuro-fuzzy. Firstly, CT and MR source images are decomposed using the NSST to obtain low and high frequency sub-bands. Maximization of absolute value is performed to fuse low frequency coefficients while high frequency coefficients are fused using the neuro-fuzzy approach. Finally, the inverse NSST is performed to gain the fused image. To assess the performance of the proposed method, several experiments are carried on different medical CT and MR image datasets. Subjective and objective assessments reveal that the proposed scheme produces better results in various quantitative criterions compared to other existing methods.

Keywords

Multimodal image fusion Non-subsampled shearlet transform Neuro-fuzzy 

Notes

Acknowledgment

The authors would like to thank Dr S. Rajkuma, the VIT University, Vellore-India, for providing image datasets of patients at different modalities.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haithem Hermessi
    • 1
    Email author
  • Olfa Mourali
    • 1
  • Ezzeddine Zagrouba
    • 1
  1. 1.Research Team SIIVA - LIMTIC Laboratory, Higher Institute of Computer ScienceUniversity of Tunis El ManarTunisTunisia

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