Fusion of Medical Images in Wavelet Domain
A Discrete Mathematical Model
Introduction: Image compression is a great instance for operations in the medical domain that leads to better understanding and implementations of treatment, especially in radiology. Discrete wavelet transform (dwt) is used for better and faster implementation of this kind of image fusion.
Methodology: To access the great feature of mathematical implementations in the medical domain we use wavelet transform with dwt for image fusion and extraction of features through images.
Results: The predicted or expected outcome must help better understanding of any kind of image resolutions and try to compress or fuse the images to decrease the size but not the pixel quality of the image.
Conclusions: Implementation of the dwt mathematical approach will help researchers or practitioners in the medical domain to attain better implementation of the image fusion and data transmission, which leads to better treatment procedures and also decreases the data transfer rate as the size will be decreased and data loss will also be manageable.
Originality: The idea of using images may decrease the size of the image, which may be useful for reducing bandwidth while transmitting the images. But the thing here is to maintain the same quality while transmitting data and also while compressing the images.
Limitations: As this is a new implementation, if we have committed any mistakes in image compression of medical-related information, this may lead to treatment faults for the patient. Image quality must not be reduced with this implementation.
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Hong ZHENG, Dequen ZHENG Yanxianag HU, Sheng Li. Study on the Optimal Parameters of Image Fusion Based on Wavelet Transform[J]. Journal of Computational Information Systems (2010) 131-137.
Smt. G. Mamatha, L. Gayatri, “An Image Fusion Using Wavelet And Curvelet Transforms”, Global Journal of Advanced EngineeringTechnologies, 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. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.35.8418&rep=rep1&type=pdf
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. https://doi.org/10.1016/s1566-2535(01)00038-0
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. https://doi.org/10.1109/iembs.2004.1403455
A. H. Gunatilaka and B. A. Baertlein, “Feature-leve 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. https://doi.org/10.1109/34.927459
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 https://doi.org/10.1109/tgrs.2010.2087029
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 https://doi.org/10.5120/ijais12-450662
Ajeesh P Sasi, Dr. LathaParameswaran, “Image Fusion Technique using DT-CWT”, in proceeding of IEEE
https://doi.org/10.1109/imac4s.2013.6526400
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 RatnaChowdary “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. https://doi.org/10.1109/cict.2013.6558217
Bull, D.R. Canagarajah, C.N. Halliwell, M. Wells, P.N.T. and Nikolov S.G. “Image fusion using a 3-D wavelet transform”, Image Processing And Its Applications, 1999. Seventh International Conference on (Conf. Publ. No. 465) (Volume:1 ) Jul 1999, Page(s): 235 - 239 vol.1 https://doi.org/10.1049/cp:19990318
A. 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. https://doi.org/10.1007/978-3-642-21783-8_6
