f Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform
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Research Article |

Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform

Author(s): M. R. Ezilarasan*, S. Vishnupriya, Thulasi Arumugam, B. Sakthi Karthi Duraiand S. Manimekalai

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 3

Publisher : FOREX Publication

Published : 30 July 2024

e-ISSN : 2347-470X

Page(s) : 836-841

DOI: https://doi.org/10.37391/IJEER.120314

Abstract

Image fusion is an extensively used technique in various areas like computer visualization, enhanced diagnostic imaging, radio therapy, automatic object recognition, image analysis, and remote sensing. The main aim of image fusion is to combine several input images into one image containing more information than the individual images. This type of image fusion results in a new image that is easier for computers and humans to see, making it possible for additional image processing operations like object detection, segmentation, and feature extraction. This paper examines the potential application of customized wavelet transform for image fusion. This paper is implemented for biomedical applications by using wavelet-based pixel-level image fusion technique and Independent Component Analysis (ICA) for de-noising. For realization of this, Initially, MRI and CT pictures are extracted using the 2D discrete wavelet transform and then ICA is used to obtain ICA bases and fused on the sub band pictures. The suggested methodology is implemented in FPGA Spartan and Virtex devices since FPGA provides a good platform for image processing in real-time and finally the performance has been compared with the existing methodologies.

Keywords: Image fusion, Bio medical, independent component analysis, discrete wavelet Transform, Field Programmable Gate Array.




M. R. Ezilarasan*, Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai-600062, Tamil Nadu India; Email: arasanezil@gmail.com

S. Vishnupriya, Assistant Professor, Department of Electronics and Communication Engineering, RMK College of Engineering and Technology, Thiruvallur, Tamil Nadu 601206, India; Email: vishnupriyaece@rmkcet.ac.in

Thulasi Arumugam, PG Resident, Department of Paediatrics Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu - 602105, India; Email: drthulasi94@gmail.com

B. Sakthi Karthi Durai, Assistant Professor Sr. Grade, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai-600062, Tamil Nadu India; Email: b.sakthikarthidurai@gmail.com

S. Manimekalai, Assistant Professor, Department of English, PSNA College of Engineering and Technology, Dindigul 624622, Tamil Nadu, India; Email: manimekalaisuresh06@gmail.com

    [1] Li, H., Manjunath, B.S. and Mitra, S.K., 1995. Multisensor image fusion using the wavelet transform. Graphical models and image processing, 57(3), pp.235-245.
    [2] Udomhunsakul, S. and Wongsita, P., 2004, December. Feature extraction in medical MRI images. In IEEE Conference on Cybernetics and Intelligent Systems, 2004. (Vol. 1, pp. 340-344). IEEE.
    [3] Yang, W. and Liu, J.R., 2013, October. Research and development of medical image fusion. In 2013 IEEE International Conference on Medical Imaging Physics and Engineering (pp. 307-309). IEEE.
    [4] Srikanth, J. and Sujatha, C.N., 2013. Image fusion based on wavelet transform for medical diagnosis. Int. Journal of Engineering Research and Applications, 3(6), pp.252-256.
    [5] Hasan, T.T., Jasim, M.H. and Hashim, I.A., 2018, December. FPGA Design and Hardware Implementation of Heart Disease Diagnosis System Based on NVG-RAM Classifier. In 2018 Third Scientific Conference of Electrical Engineering (SCEE) (pp. 33-38). IEEE.
    [6] Ruby, E. K., Amirthayogam, G., Sasi, G., Chitra, T., Choubey, A., & Gopalakrishnan, S. (2024). Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems. Mesopotamian Journal of Computer Science, 2024, 62-70.
    [7] Sheela, M. S., Chand, S. R., Gopalakrishnan, S., Gopianand, M., & Hephzipah, J. J. (2024). Empowering Aquarists a Comprehensive Study On IOT-Enabled Smart Aquarium Systems For Remote Monitoring And Control. Babylonian Journal of Internet of Things, 2024, 33-43.
    [8] Rao Borra, S.P., Panakala, R.K. and Kumar, P.R., 2019. VLSI Implementation of Image Fusion Using DWT-PCA Algorithm with Maximum Selection Rule. International Journal of Intelligent Engineering & Systems, 12(5).
    [9] Wang, A., Sun, H. and Guan, Y., 2006, April. The application of wavelet transform to multi-modality medical image fusion. In 2006 IEEE International Conference on networking, sensing and control (pp. 270-274). IEEE.
    [10] Ezilarasan, M.R., Pari, J.B., Aanandhasaravanan, K., Prasanna, N.V. and Balaji, D., 2022, April. Performance analysis of ICA algorithm for Blind Source Separation. In 2022 8th International Conference on Smart Structures and Systems (ICSSS) (pp. 1-6). IEEE.
    [11] Ezilarasan, M.R., Britto Pari, J. and Leung, M.F., 2023. High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis. Journal of Circuits, Systems and Computers, 32(17), p.2350294.
    [12] Al-Fatlawy, M. H., Sheela, M. S., Yadav, S. K., Srinivasan, V., Gopalakrishnan, S., & Reddy, N. U. (2023, August). Research on Graphic Design Image Processing Technology Based on Newton's method in Photoshop. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 710-715). IEEE.
    [13] Gopalakrishnan, S., Sheela, M. S., Saranya, K., & Hephzipah, J. J. (2023). A Novel Deep Learning-Based Heart Disease Prediction System Using Convolutional Neural Networks (CNN) Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 516-522.
    [14] Ezilarasan, M.R. and Pari, J.B., 2022. An Optimized Pipeline Based Blind Source Separation Architecture for FPGA Applications. Int. J. Electr. Electron. Res., 10, pp.632-638.
    [15] Ezilarasan, M.R., Britto Pari, J. and Leung, M.F., 2023. Reconfigurable Architecture for Noise Cancellation in Acoustic Environment Using Single Multiply Accumulate Adaline Filter. Electronics, 12(4), p.810.
    [16] Mishra, A., Mahapatra, S. and Banerjee, S., 2017. Modified Frei-Chen operator-based infrared and visible sensor image fusion for real-time applications. IEEE Sensors Journal, 17(14), pp.4639-4646.
    [17] Pemmaraju, M., Mashetty, S.C., Aruva, S., Saduvelly, M. and Edara, B.B., 2017, May. Implementation of image fusion based on wavelet domain using FPGA. In 2017 International Conference on Trends in Electronics and Informatics (ICEI) (pp. 500-504). IEEE.
    [18] Borra, S.P.R., Panakala, R.K. and Kumar, P.R., 2019. VLSI implementation of image fusion using DWT-PCA algorithm with maximum selection rule. International Journal of Intelligent Engineering and Systems, 12(5).
    [19] Bhaskar, P.C. and Munde, V.R., 2017, August. FPGA implementation of non-subsampled Shearlet transform for image fusion. In 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) (pp. 1-6). IEEE.

M. R. Ezilarasan, S. Vishnupriya, Thulasi Arumugam, B. Sakthi Karthi Durai, S. Manimekalai (2024), Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform. IJEER 12(3), 836-841. DOI: 10.37391/IJEER.120314.