Efficient Fusion Scheme for Multi-Modality Images | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 64, Volume 28, ICEEM2019-Special Issue, 2019, Page 269-274 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.77377 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Zeinab Z. El Kareh1; Ghada M. El Banby, Essam Emadbouly2 | ||||
| 1Menoufia University Faculty Of Electronic Engineering, Menouf Department Of Electronics And Communications Engineering menoufiya, MENOUF | ||||
| 2Menoufia University Faculty Of ElectronicEngineering, Menouf Department Of Industrial Electronics And Control menoufiya, MENOUF | ||||
| Abstract | ||||
| this paper presents an optimum approach for medical wavelet based Principa1 Component Ana1ysis (PCA) to modify central fusion using different wavelet families and force optimization (MCFO) technique. The proposed MCFO optimized wavelet based fusion algorithm provides the optimum gain parameters values for fusion that achieves the highest image quality and best visualization. The modalities adopted are Magnetic Resonance imaging (MRI), Positron Emission Tomography (PET) as a type of Computed Tomography (CT) modalities. A comparative study is held between the proposed algorithms and the traditional (DWT) fusion rule then evaluated subjectively and objectively with different Evaluation metrics such as entropy, edge intensity, contrast, standard deviation, (PSNR), and average gradient have been adopted for performance evaluation of the proposed method. The obtained results confirm that the proposed method is superior in performance to the DWT and PCA methods individually. | ||||
| Keywords | ||||
| Modified central force optimization; Principa1 Component Ana1ysis, Discrete Wavelet Transform, average gradient | ||||
| References | ||||
|
[1] Deserno T. M., “Fundamentals of Biomedical Image Processing”, Biological and Medical Physics, Biomedical Engineering, DOI: 10.1007/978-3-642-15816-2 1, Springer Verlag Berlin Heidelberg, 2011.
[2] A.P. James, and B.V. Dasarathy, “Medical Image Fusion: A survey of the State of the Art”, Information Fusion, Vol. 19, pp. 4-19, 2014.
[3] El-Gamal, Fatma El-Zahraa Ahmed, Mohammed Elmogy, Ahmed Atwan, "Current trends in medical image registration and fusion", Egyptian Informatics Journal, vol. 17, no. 1, pp. 99-124, 2016.
[4] Singh T., Kaur M., Kaur A., “A detailed Comparative Study of Various Image Fusion Techniques used in Digital Images”, International Journal of Advanced Engineering Technology, E-ISSN 0976-3945, 2013.
[5] J. Joseph, and A. Barhatte, “Medical Image Fusion Based on Wavelet Transform and Fast Curvelet Transform”,IJEDR, Vol. 2, No. 1, 2014.
[6] Sivakumar N., Helenprabha K., “Hybrid medical image fusion using wavelet and curvelet transform with multi-resolution processing”, Biomedical Research, Vol. 28, Issue 6, 2017.
[7] Nabil A., Taha T. E., and Faragallah O. S., “Study of Advanced Fusion Methods in Medical Image Processing”, M. Sc. Thesis, Faculty of Electronic Engineering, Menoufia University, 2014.
[8] H. R. Shahdoosti, H. Ghassemian,“Combining the Spectral PCA and Spatial PCA Fusion Methods by an Optimal Filter”TarbiatModares University,June 2015.
[9] Matthew Bick, "Central Force Optimization - Analysis of Data Structures & Multiplicity Factor", Master Thesis,University of Toledo, December 2015.
[10] KoranyRagab Mahmoud, "Synthesis of unequally-spaced linear arrayusing modified central force optimisationalgorithm", IET Microwaves, Antennas & Propagation, Vol. 10, Issue. 10, pp. 1011–1021, 2016.
[11] Daniel E., Anitha J., Kamaleshwaran K.K., Rani I. “Optimum spectrum mask based medical image fusion using GrayWolf Optimization”, Biomedical Signal Processing and Control, pp. 36–43, 2017.
[12]I.T. Jollife, “Principa1 Component Analysis _2nd edition”, ISBN: 978-0-387-95442-4, Inc. Printed in the United States of America, 2002.
[13] Omar Z., Stathaki T., “Image Fusion: An Overview”, International Conference on Intelligent Systems, Modelling and Simulation, DOI 10.1109, 2014.
[14] Srinivasu Polinati, Ravindra Dhuli,"A Review on Multi-Model Medical Image Fusion",(ICCSP), IEEE, 2019.
[15] Kannan K., Perumal S.A., Arulmozhi K., "Optimal Decomposition Level of Discrete Wavelet Transform for Pixel Based Fusion of Multi-Focused Images", International Conference on Computational Intelligence and Multimedia Applications, pp. 314-318, Dec., 2007.
[16] Li S., Yang B., Hu J., "Performance Comparison of Different Multi-Resolution Transforms for Image Fusion", Information Fusion, Vol. 12, No. 2, pp. 74-84, 2011.
[17] Pratima Manhas,"Image processing by using different types of Discrete wavelet transform", ICAICR-2018(shimla), At shimla.
[18] Israa Abdul-Ameer,Jieqing Tan, ZhengfengHou, “Adaptive PCA-SIFT Matching Approach for Face Recognition Application”, Internationa1Multi-Conferenceof Engineers and Computer Scientists IMECS,Vol I, March 12 - 14, Hong Kong, 2014.
[19] https://medpix.nlm.nih.gov/home. Last access 10/7/2017.
[20] G. N. Raut, P. L. Paikrao, D. S. Chaudhari, "A Study of Quality Assessment Techniques for Fused Images," IJITEE, Vol. 2, No. 4, 2013.
[21] P. Jagalingam, ArkalVittalHegde, "A Review of Quality Metrics for Fused Image", Aquatic Procedia, Vol. 4, pp. 133 – 142, 2015.
[22] V.R.S Mani, S. rivazhagan, "Survey of Medical Image Registration", Journal of Biomedical Engineering and Technology, pp. 8-25, 2013.
[23]Eman A. Donia, El-Sayed M. EL-Rabaie, Gh. M. El-Banby, Osama S. Faragallah, Fathi E. Abd El-Samie," Infrared Image Enhancement Based on BothHistogram Matching And Wavelet Fusion", Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), pp. 111-114, 2016. | ||||
|
Statistics Article View: 135 |
||||
