I. J. of Electrical & Electronics Research
Research Article | 
Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications
Author(s) : N. Shyamala1 and Dr. S. Geetha2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2 , Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 30 May 2022
e-ISSN : 2347-470X
Page(s) : 161-166
Abstract
Medical image compression becomes necessary to efficiently handle huge number of medical images for storage and transmission purposes. Wavelet transform is one of the popular techniques widely used for medical image compression. However, these methods have some limitations like discontinuity which occurs when reducing image size employing thresholding method. To overcome this, optimization method is considered with the available compression methods. In this paper, a method is proposed for efficient compression of medical images based on integer wavelet transform and modified grasshopper optimization algorithm. Medical images are pre-processed using hybrid median filter to discard noise and then decomposed using integer wavelet transform. The proposed method employed modified grasshopper optimization algorithm to select the optimal coefficients for efficient compression and decompression. Four different imaging techniques, particularly magnetic resonance imaging, computed tomography, ultrasound, and X-ray, were used in a series of tests. The suggested method's compressing performance is proven by comparing it to well-known approaches in terms of mean square error, peak signal to noise ratio, and mean structural similarity index at various compression ratios. The findings showed that the proposed approach provided effective compression with high decompression image quality.
Keywords: Integer Wavelet Transform, Modified Grasshopper Optimization Algorithm, Optimization, Telemedicine Applications
N. Shyamala, Research Scholar, Government Arts and Science College, Udumalpet, India; Email: shyamalafeb20@gmail.com
Dr. S. Geetha, Assistant Professor, Government Arts and Science College for Women, Puliakulam, Coimbatore, India
[1] M.H. Alkinami, E.A. Zanaty and S.M. Ibrahim, “Medical image compression based on wavelet with particle swarm optimization”, Computers, Materials and continua, vol. 67, no. 2, pp. 1577-1592, 2021.[Cross Ref]
[2] K.M. Hosny, A.M. Khalid and E.R. Mohamed, “Optimized medical image compression for telemedicine applications”, In: Masmoudi M., Jarboui B., Siarry P. (eds) Artificial Intelligence and Data Mining in Healthcare. Springer, Cham, 2021.[Cross Ref]
[3] P. Sreenivasulu and S. Varadarajan, “An efficient lossless ROI image compression using wavelet-based modified region growing algorithm”, Journal of Intelligent Systems, vol. 29, no. 1, pp. 1063-1078, 2020.[Cross Ref]
[4] R. Haridoss and S. Punniyakodi, “Compression and enhancement of medical images using opposition based harmony search algorithm”, Journal of Information Processing Systems, vol. 15, no. 2, pp. 288-304, 2019.[Cross Ref]
[5] P.N.T. Ammah and E. Owusu, “Robust medical image compression based on wavelet transform and vector quantization”, Informatics in Medicine Unlocked, vol. 15, 2019.[Cross Ref]
[6] P. Sreenivasulu, S.V. Rajan and S. Thenappan, “Medical image compression by optimal filter coefficients Aided Transforms using modified rider optimization algorithm”, International Journal. Engg and Adv. Tech, vol. 9, no. 2, 2019.[Cross Ref]
[7] K.M. Honsy, A.M. Khalid and E.R. Mohamed, “Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony”, Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 385-398, 2018.[Cross Ref]
[8] S. Saremi, S. Mirjalili and A. Lewis, “Grasshopper optimization algorithm: Theory and Application”, Advances in Engineering Software, vol. 105, pp. 30-47, 2017.[Cross Ref]
[9] M.S. Wu, “Genetic algorithm based on discrete wavelet transformation for fractal image compression”, Journal of Visual Communication and Image Representation, vol. 25, no. 8, pp. 1835-1841, 2014.[Cross Ref]
[10] M. Rehman, M. Sharif and M. Raza, “Image compression: A survey”, Research Journal of Applied Sciences, Engineering and Technology, vol. 7, no. 4, pp. 656-672, 2014.[Cross Ref]
[11] G. Vijayvargiya, S. Silakari and R. Pandey, “A novel medical image compression technique based on structure reference selection using integer wavelet transform function and PSO algorithm”, International Journal of Computer Applications, vol. 91, no. 11, pp. 9-13, 2014.[Cross Ref]
[12] A. Boucetta and K.E. Melkemi, “DWT based-approach for color image compression using genetic algorithm”, In International conference on image and signal processing, pp. 476-484, 2012.[Cross Ref]
N. Shyamala and Dr.S. Geetha (2022), Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications. IJEER 10(2), 161-166. DOI: 10.37391/IJEER.100219.