Main Article Content
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.
In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.
At the same time, as the author or co-author, I declare that I am completely in agreement with the conditions presented in this work and that I cede all patrimonial rights, in other words, regarding reproduction, public communication, distribution, dissemination, transformation, making it available and all forms of exploitation of the work using any medium or procedure, during the term of the legal protection of the work and in every country in the world, to the Universidad Cooperativa de Colombia Press.
Problem: This paper presents a novel approach for Multiresolution analysis.
Objective: It is most widely used in image fusion science, which captures the features of an image not only at
different resolutions, but also at different orientations.
Methodology: This Wavelet based algorithm has additional advantages of fast implementation, versatility, auxiliary memory saving, complete reconstruction properties and simplicity as wavelet transformation was used.
Results: The simulation results of the MRI and CT images show perfectly acceptable image quality and cover
disease detection in the subsequent final image.
Conclusion: Principal Component Analysis (PCA) based on fusion algorithms will empower medical researchers or clinicians to properly apply image fusion and data transmission, which leads to better care practices to minimize redundancies and can also handle data loss.
Originality: Fusing images can decrease the image size, which can decrease the bandwidth when transmitting images. This also compresses the images; here an attempt is made to retain the same consistency.
Limitations: As this is still a relatively novel method, mistakes with regard to the handling of clinical data may prompt treatment deficiencies for the patient. Image quality must not be diminished with this usage
 Yadav, S.P. Yadav, S. “Fusion of Medical Images in Wavelet Domain: A Discrete Mathematical Model”, Ingeniería Solidaría, (Vol 14, No 25) May 2018, DOI: https://doi.org/10.16925/.v14i0.2236
 Smt. G. Mamatha, L. Gayatri, “An Image Fusion Using Wavelet And Curvelet Transforms”, Global Journal of Advanced Engineering Technologies, Vol1, Issue-2, 2012, ISSN: 2277-6370.
 R. K. Sharma, “Probabilistic Model-based Multisensor Image Fusion”, PhD thesis, Oregon Graduate Institute of Science and Technology, Portland, Oregon, 1999.
 S. Li, J. T. Kwok, and Y. Wang, “Combination of images with diverse focuses using the spatial frequency”, Information Fusion 2, pp. 169–176, 2001.
 Sekhar AS, Prasad MG. A novel approach of image fusion on MR and CT images using wavelet transforms. In 2011 3rd International Conference on Electronics Computer Technology (ICECT), 2011 ;( 4):172-176). IEEE.
 S. Kor and U. Tiwary, “Feature level fusion of multimodal medical images in lifting wavelet transform domain” IEEE International Conference of the Engineering in Medicine and Biology Security, pp. 1479–1482, 2004.
 A. H. Gunatilaka and B. A. Baertlein, “Feature-level and decision-level fusion of non-coincidently sampled sensors for land mine detection” IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), pp. 577–589, 2001.
 Daljit Kaur, P S Mann “Medical Image Fusion Using Gaussian Filter, Wavelet Transform and Curvelet Transform Filtering” International Journal of Engineering Science & Advanced Technology, Volume-4, Issue-3, 252-256 ISSN: 2250-3676.
 MiloudChikr El-Mezouar, NasreddineTaleb, KidiyoKpalma, and Joseph Ronsin “An IHS-Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery”, IEEE Transactions on Geo-science And Remote Sensing, vol. 49, No. 5, May 2011.
 Chetan K. Solanki, Narendra M. Patel, ―Pixel based and Wavelet based Image fusion Methods with their Comparative Study”, National Conference on Recent Trends in Engineering & Technology.
 RudraPratap Singh Chauhan,RajivaDwivedi and Sandeep Negi “ Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 4– No.2, September 2012 – www.ijais.org.
 Ganasala P, Kumar V. CT and MR image fusion scheme in non-sub sampled contourlet transform domain. J Digit Imaging. 2014; 27(3):407‐418. Doi: 10.1007/s10278-013-9664-x.
 Kanisetty Venkata Swathi, CH.HimaBindu “Modified Approach of Multimodal Medical Image Using Daubechies Wavelet Transform” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 11, November 2013, ISSN (Print): 2319-5940.
 J. Srikanth*, C.N Sujatha “Image Fusion Based on Wavelet Transform for Medical Diagnosis”, Int. Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.252-256.
 Ch.Bhanusree, P. Aditya Ratna Chowdary “A Novel Approach of image fusion MRI and CT image using Wavelet family”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 8, August 2013, ISSN 2319 – 4847.
 Kanaka Raju Penmetsa, V.G.Prasad Naraharisetti, N.Venkata RAO “An Image Fusion Technique For Color Images Using Dual-Tree Complex Wavelet Transform”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 8, October – 2012 ISSN: 2278-0181.
 Patil Gaurav Jaywantrao, Shabahat Hasan, “Application of Image Fusion Using Wavelet Transform In Target Tracking System”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181Vol. 1 Issue 8, October – 2012.
 Pavithra C, Dr. S. Bhargavi, “Fusion of Two Images Based on Wavelet Transform”, International Journal of Innovative Research in Science, Engineering and Technology. 2, Issue 5, May 2013.
 Singh, R., Khare, A. “Multimodal medical image fusion using daubechies complex wavelet transform”, Information & Communication Technologies (ICT), 2013 IEEE Conference on , April 2013 Page(s):869 - 873 Print ISBN:978-1-4673- 5759-3.
 Ghanapriya Singh, Tarun Kumar Rawat, “Color Image Enhancement by Linear Transformations Solving out of Gamut Problem,” International Journal of Computer Applications (USA), ISSN: 0975-8887, Vol.-6, No-14, pp.28-32, April 2013.
 Ai Deng, Jin Wu, Shen Yang “An Image Fusion Algorithm Based on Discrete Wavelet Transform and Canny Operator” Advanced Research on Computer Education, Simulation and Modeling Communications in Computer and Information Science Volume 175, 2011, pp 32-38.
 Cvejic N, Bull D, Canagarajah N. Region-based multimodal image fusion using ICA bases. IEEE Sensors Journal. 2007; 7(5/6):743.
 P. S. Sengar, T. K. Rawat and H. Parthasarathy, "Color image enhancement by scaling the discrete wavelet transform coefficients," 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy, Kanjirapally, 2013, pp. 1-6. doi: 10.1109/AICERA-ICMiCR.2013.6575994.
 Deepali Aneja, Tarun Kumar Rawat, “Fuzzy Clustering Algorithms for Effective Medical Image Segmentation,” International Journal of Intelligent Systems and Applications (Hong-Kong), 2013, 11, 55-61, DOI: 10.5815/ijisa.2013.11.06.
 Pradnya PM, Sachin DR. Wavelet based image fusion techniques. In 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), 2013;77-81.
 Li H, Manjunath BS, Mitra SK. Multisensor image fusion using the wavelet transform. Graphical models and image processing. 1995;57(3):235-45.
 Pajares G, De La Cruz JM. A wavelet-based image fusion tutorial. Pattern recognition. 2004; 37(9):1855-72.
 Na Y, Ehlers M, Yang W, Shi L. Adaptive remote sensing image fusion with multiwavelet transform. In Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V, International Society for Optics and Photonics. 2005; 5983:598302.