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An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

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Abstract

This paper introduces an optimal solution for wavelet-based medical image fusion using different wavelet families and Principal Component Ana1ysis (PCA) based on the Modified Central Force Optimization (MCFO) technique. The main motivation of this work is to increase the quality of medical fused images in order to provide correct diagnosis of diseases for the objective of optimal therapy. This can be achieved by fusing medical images of different modalities using an optimization technique based on the MCFO. The MCFO technique gives the optimum gain parameters that achieve the best fused image quality. Histogram matching is applied to improve the overall values of the Peak Signal-to-Noise Ratio (PSNR), entropy, local contrast, and quality of the fused image. A comparative study is performed between the proposed algorithm, the traditional Discrete Wavelet Transform (DWT), and the PCA fusion using maximum fusion rule. The proposed algorithm is evaluated subjectively and objectively with different fusion quality metrics. Simulation results demonstrate that the proposed MCFO optimized wavelet-based fusion algorithm using Haar wavelet and histogram matching achieves a superior performance with the highest image quality and clearest image details in a very short processing time.

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References

  1. Abdul-Ameer I, J Tan, Z Hou (2014) “Adaptive PCA-SIFT Matching Approach for Face Recognition Application”, Internationa1Multi-Conferenceof Engineers and Computer Scientists IMECS,Vol I, March 12–14, Hong Kong

  2. Arunvinodh C, Davis AM (2015) "Comparative Analysis of Transform-Based Image Fusion Techniques for Medical Applications", IEEE Int. Conf. on Innovations in Information, Embedded and communication systems (ICIIECS). 1–6

  3. Bick M (2015) "Central Force Optimization - Analysis of Data Structures & Multiplicity Factor", Master Thesis,University of Toledo, December

  4. Daniel E, Anitha J, Kamaleshwaran KK, Rani I (2017) Optimum spectrum mask based medical image fusion using GrayWolf optimization. Biomed Sign Proc Control:36–43

  5. Deserno TM (2011) “Fundamentals of Biomedical Image Processing”, Biological and Medical Physics, Biomedical Engineering, DOI: https://doi.org/10.1007/978-3-642-15816-2 1, Springer Verlag Berlin Heidelberg

  6. Donia EA, E-SM EL-Rabaie, GhM El-Banby, OS Faragallah, FE Abd El-Samie (2016) Infrared Image Enhancement Based on BothHistogram Matching And Wavelet Fusion, Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC). 111–114

  7. Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aqua Proc 4:133–142

    Article  Google Scholar 

  8. James AP, Dasarathy BV (2014) Medical image fusion: a survey of the state of the art. Inform Fus 19:4–19

    Article  Google Scholar 

  9. Joseph J, Barhatte A (2014) “Medical Image Fusion Based on Wavelet Transform and Fast Curvelet Transform”,IJEDR, Vol. 2, No. 1

  10. Kannan K., Perumal S.A., Arulmozhi K. (2007) "Optimal Decomposition Level of Discrete Wavelet Transform for Pixel Based Fusion of Multi-Focused Images", International Conference on Computational Intelligence and Multimedia Applications. 314–318

  11. Lee DTL, Yamamoto A (1994) "Wavelet Analysis: Theory and Applications", Hewlett-Packard Journal

  12. Leng L, Zhang J, Xu J, Khan K, Alghathbar K (2010) " Dynamic Weighted Discrimination Power Analysis in DCT Domain for Face and Palmprint Recognition ", International Conference on Information and Communication Technology Convergence (ICTC), Jeju, South Korea, 17–19

  13. Leng L, J Zhang, G Chen, MK Khan, K Alghathbar (2011) " Two-Directional Two-Dimensional Random Projection and Its Variations for Face and Palmprint Recognition", International Conference on Computational Science and Its Applications. 458–470

  14. Leng L , J Zhang, G Chen, K Khan, P Bai (2011) " Two Dimensional PalmPhasor Enhanced by Multi-orientation Score Level Fusion", International Conference on Secure and Trust Computing, Data Management, and Application, pp. 122–129

  15. Leng L, S Zhang, X Bi, MK Khan (2012) " Two-Dimensional Cancelable Biometric Scheme", International Conference on Wavelet Analysis and Pattern Recognition, Xian, China

  16. Leng L, M Li, L Leng, ABJ Teoh (2013) " Conjugate 2DPalmHash Code for Secure Palm-Print-Vein Verification", 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China, 16–18

  17. Leng L, Li M, Kim C, Bi X (January 2017) Dual-source discrimination power analysis for multi-instance contactless Palmprint recognition. Multimed Tools Appl 76(1):333–354

    Article  Google Scholar 

  18. Li S, Yang B, Hu J (2011) Performance comparison of different multi-resolution transforms for image fusion. Inform Fus 12(2):74–84

    Article  Google Scholar 

  19. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) " Action2Activity: Recognizing Complex Activities from Sensor Data", IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence. 1617–162

  20. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  21. Mahmoud KR (2016) Synthesis of unequally-spaced linear arrayusing modified central force optimisation algorithm. IET Microw, Anten Propagation 10(10):1011–1021

    Article  Google Scholar 

  22. Mani VRS, Rivazhagan S (2013) "Survey of Medical Image Registration", Journal of Biomedical Engineering and Technology. 8–25

  23. Mitchell HB (2010) “Image Fusion: Theories, Techniques and Applications”, ISBN 978–3–642-11215-7, Springer-Verlag Berlin Heidelberg

  24. Nabil A, Taha TE, Faragallah OS (2014) “Study of Advanced Fusion Methods in Medical Image Processing”, M. Sc. Thesis, Faculty of Electronic Engineering, Menoufia University

  25. Omar Z, Stathaki T (2014) “Image Fusion: An Overview”, International Conference on Intelligent Systems, Modelling and Simulation

  26. Rajini KC, Roopa S (2017) "A Review on Recent Improved Image Fusion Techniques", IEEE Int. Conf. on Wireless Communications, Signal Processing and Networking. 149–153

  27. Raut GN, PL Paikrao, D S Chaudhari (2013) A Study of Quality Assessment Techniques for Fused Images. IJITEE 2, No. 4

  28. Shahdoosti HR, Ghassemian H (2015) “Combining the Spectral PCA and Spatial PCA Fusion Methods by an Optimal Filter”TarbiatModares University

  29. Patne G, Ghonge P, Tuckley K (2017) Review of CT and PET Image Fusion Using Hybrid Algorithm. I2C2

  30. Song H, Qiu P (2017) Intensity-based 3D local image registration. Pattern Recogn Lett 94:15–21

    Article  Google Scholar 

  31. Wang A, Sun H, Guan Y (2006) "The Application of Wavelet Transform to Multi-Modality Medical Image Fusion", IEEE International Conference on Networking, Sensing and Control. 270–274

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Benha University, Benha, Egypt

    Heba M. El-Hoseny & Wael A. Mohamed

  2. Department of Industrial E1ectronics and Control Engineering, Facu1ty of E1ectronic Engineering, Menoufia University, Menouf, Egypt

    Zeinab Z. El Kareh, Ghada M. El Banby & Essam El-Madbouly

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

    Korany R. Mahmoud

  4. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Osama S. Faragallah

  5. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Osama S. Faragallah

  6. Department of Information Technology, College of Computers and Information Technology, Taif University, Al-Hawiya, 21974, Saudi Arabia

    S. El-Rabaie & Fathi E. Abd El-Samie

Authors
  1. Heba M. El-Hoseny
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  2. Zeinab Z. El Kareh
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  3. Wael A. Mohamed
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  4. Ghada M. El Banby
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  5. Korany R. Mahmoud
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  6. Osama S. Faragallah
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  7. S. El-Rabaie
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  8. Essam El-Madbouly
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  9. Fathi E. Abd El-Samie
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Correspondence to Zeinab Z. El Kareh.

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El-Hoseny, H.M., El Kareh, Z.Z., Mohamed, W.A. et al. An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching. Multimed Tools Appl 78, 26373–26397 (2019). https://doi.org/10.1007/s11042-019-7552-1

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  • DOI: https://doi.org/10.1007/s11042-019-7552-1

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