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Optimized multimodal medical image fusion framework using multi-scale geometric and multi-resolution geometric analysis

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Abstract

For proper homoeopathic identification of the medical image, image fusion has been proposed as a mandatory solution to obtain high-spectral and high-spectral spatial data. This article presents a complete fusion system for several types of medical images according to their multi-resolution, multi-scale transforms and the Modified Central Force Optimization (MCFO) technique. Four main techniques have been proposed for this purpose; Optimized Discrete Wavelet and Dual-Tree based fusion techniques as a multi-resolution transform. Besides, the optimized Non-Sub-Sampled Contourlet and Non-Sub-Sampled Shearlet as multi-scale fusion techniques. The perfect matching between input images and minimum artifacts after image registration can be achieved through four stages in the proposed fusion algorithms. First, the input medical image is initially decomposed into their coefficients, and the MCFO method establishes the optimal gain parameter values of the resulted coefficients. Finally, the adaptive histogram equalization and the histogram matching are applied for higher clearness and better visualization of information details. The proposed algorithms are evaluated using various datasets for different medical and surveillance applications through some quality metrics. The Experimental test outcomes indicate that the proposed fusion algorithms achieve good performance with high image quality and appreciated estimation metrics principles. Moreover, it provides better image visualization and minimum processing time, which helps diagnose diseases.

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Acknowledgements

This study was funded by the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/08), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Osama S. Faragallah

  2. Department of Electronics and Electrical Communication Engineering, Al-Obour High Institute for Engineering and Technology, Cairo, 3036, Egypt

    Heba El-Hoseny

  3. Department of Electronics & Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Walid El-Shafai, El-Sayed El-Rabaie & Fathi Abd El-Samie

  4. Electrical Engineering Department, Faculty of Engineering, Benha University, Benha, 13512, Egypt

    Wael Abd El-Rahman & Gamal G. N. Geweid

  5. Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt

    Hala S. El-sayed

  6. Department of Electronics and Communications, Faculty of Engineering, Helwan University, Cairo, Egypt

    Korany R. Mahmoud

  7. Department of Engineering and Technology, Worldwide College of Aeronautics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA

    Gamal G. N. Geweid

Authors
  1. Osama S. Faragallah
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  2. Heba El-Hoseny
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  3. Walid El-Shafai
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  4. Wael Abd El-Rahman
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  5. Hala S. El-sayed
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  6. El-Sayed El-Rabaie
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  7. Fathi Abd El-Samie
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  8. Korany R. Mahmoud
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  9. Gamal G. N. Geweid
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Correspondence to Osama S. Faragallah.

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Faragallah, O.S., El-Hoseny, H., El-Shafai, W. et al. Optimized multimodal medical image fusion framework using multi-scale geometric and multi-resolution geometric analysis. Multimed Tools Appl 81, 14379–14401 (2022). https://doi.org/10.1007/s11042-022-12260-0

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  • DOI: https://doi.org/10.1007/s11042-022-12260-0

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