An Effective Selection of DCT and DWT Coefficients for an Adaptive Medical Image Compression Technique Using Multiple Kernel FCM

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Over the last two decades, great developments have been made in image compression approaches driven by a growing demand for storage and transmission of visual information. However, a number of publications have demonstrated good results of the compressed domain approach to image analysis. Nevertheless, very little work has been carried out on ROI based compression. In this paper, we have proposed an adaptive approach to compress the image without lossless version using selection of DCT and DWT coefficients and MKFCM. Our proposed approach consists of three stages, (i) Region segmentation (ii) Image compression (iii) Image decompression. At first, the input medical image is segmented by ROI, Non-ROI and background using MKFCM. Subsequently, the ROI region compressed by DCT and SPIHT coding and Non-ROI region is compressed by DWT and Huffman coding. The non-relevant regions are directly converted to zero. From the compressed regions like as ROI, Non-ROI and background, finally we obtain compression ratio to evaluate the proposed approach. Then, in decompression stage, the original medical image is extracted using the devised procedure. We can also see that our proposed image compression approach have outperformed by having better compression ratio of 4.6446 when compared existing technique only achieved 4.4326
Copyright © 2014 Praise Worthy Prize - All rights reserved.


Image Compression; Discrete Wavelet Transform; Discrete Cosine Transform; ROI; Background; SPIHT; Huffman

Full Text:



V.K. Bairagi A.M. Sapkal, "Automated region-based hybrid compression for digital imaging and communications in medicine magnetic resonance imaging images for telemedicine applications", IET Sci. Meas. Technol., Vol. 6, No. 4, pp. 247–253, 2012.

Long Chen, C. L. Philip Chen, and Mingzhu Lu, "A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation", IEEE transactions on systems, man, and cybernetics-part b: cybernetics, pp. 1263 - 1274, vol. 41, no. 5, 2011.

Ansari and Anan, "Recent Trends in Image Compression and Its Application in Telemedicine and Teleconsultation", National Systems Conference, pp. 59-64, 2008.

Sadashivappa and AnandaBabu, "Evaluation of Wavelet Filters for Image Compression", World Academy of Science, Engineering and Technology, Vol. 51, pp. 131-137, 2009.

Sonja Grgic, Mislav Grgic and Branka Zovko-Cihlar, “Performance Analysis of Image Compression Using Wavelets", IEEE Transactions on Industrial Electronics, Vol. 48, No. 3, pp. 682-695, June 2001.

Loganathan and .Kumaraswamy, "Medical Image Compression Using Biorthogonal Spline Wavelet with Different Decomposition", International Journal on Computer Science and Engineering Vol. 02, No. 09, pp. 3003-3006, 2010.

Yao-Tien Chen and Din-Chang Tseng, “Wavelet-based medical Image compression with adaptive prediction”, In proceedings of International symposium on Intelligent Signal Processing and Communication Systems, Vol. 31, pp.1-8, 2007.

Krishnan K.marcellin MW, Bilgin A, Nadar M, ”Prioritization of compressed data by tissue type using JPEG2000”, In proceedings of SPIE medical imaging, Vol. 5748, pp. 181-189, 2005.

Ruchika, Mooninder Singh and Anant Raj Singh, “Compression of Medical Images Using Wavelet Transforms”, International Journal of Soft Computing and Engineering (IJSCE), Vol.2, No.2, pp. 2231-2307, 2012.

D.A. Karras, S.A. Karkanis´ and D. E. Maroulis, “Efficient Image Compression of Medical Images Using the Wavelet Transform and Fuzzy c-means Clustering on Regions of Interest”, Proceedings of the 26th EUROMICRO Conference (EUROMICRO'00)-Vol. 2, pp.2469, 2000.

Dimitrios A. Karras, “Efficient Medical Image Compression/Reconstruction Applying the Discrete Wavelet Transform on Texturally Clustered Regions”, International Workshop on Imaging Systems and Techniques, 2005.

M.Tamilarasi and V. Palanisamy, “Medical Image Compression Using Fuzzy C-Means Based Contourlet Transform”, Journal of Computer Science, Vol. 7, No. 9, pp.1386-1392, 2011.

Shaou-Gang Miaou, Fu-Sheng Ke, and Shu-Ching Chen, “A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding”, IEEE Transactions On Information Technology In Biomedicine, Vol. 13, No. 5, 2009.

T.T. Dang, S.K. Nguyen, T.D. Vu and S. Higuchi, “Cross-point regions on multiple bit planes for lossless images compression”, IET Image Processing,Vol. 5, No. 5, pp. 466–471, 2011.

Jonathan Taquet and Claude Labit, “Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images”, IEEE Transactions on Image Processing, Vol. 21, No. 5, 2012.

Walaa M. Abd-Elhafiez and Wajeb Gharibi, “Color Image Compression Algorithm Based on the DCT Blocks”, IJCSI International Journal of Computer Science Issues, Vol. 9, No. 4, No 3, 2012.

Rongchang Zhao and YIDE Ma,“A region segmentation method for region-oriented image compression”, Journal of Neurocomputing, Vol.85, pp.45-52, 2012.

M. Beladgham, I. Boucli Hacene, A. Taleb-Ahmed and M. Khélif, “MRI Images Compression Using Curvelets Transforms”, AIP Conference Proceedings, Vol. 1019, No. 1, pp.249, 2008.

Xiwen Zhao and Zhihai He, “Lossless image compression using super-spatial prediction of structural components”, In Proceedings of the 27th conference on Picture Coding Symposium, pp. 393-396, Sue (3): 2013 289,2009.

Miaou, S.-G., Ke, F.-S., Chen, S.-C.: ‘A lossless compression method for medical image sequences using JPEG-LS and interframe coding’, IEEE Trans. Inf. Technol. Biomed., Vol.13, No.5, pp. 818-821,2009.

Baeza, I., Verdoy, A.: ‘ROI-based procedures for progressive transmission of digital images: a comparison’, J. Math. Comput. Model. Vol.50, pp. 849-859, 2009.

Sujatha, R., Ramakrishnan, M., Developing an effective and compressed hybrid signcryption technique utilizing huffman text coding procedure, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2940-2947.

Umaamaheshvari, A., Prabhakaran, K., Thanushkodi, K., Watermarking of medical images with optimized biogeography, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2974-2984.

Ghatasheh, N.A., Abu-Faraj, M.M., Faris, H., Dead sea water level and surface area monitoring using spatial data extraction from remote sensing images, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2892-2897.

Padmalal, S., Nelson Kennedy Babu, C., Automatic feature extraction using replica based approach in digital fundus images, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2917-2924.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize