The International Arab Journal of Information Technology (IAJIT)



Telemedicine is one of the emerging fields in medi cine which is characterized by transmitting medical data and images between different users. The medical images which are transmitted over the internet require huge bandwidth. Even images of single patient are found to be very huge in size due to resolution factor and number of imag es per diagnosis. So, there is an immense need for efficient compression techniques that can be used to compress these medic al images. In medical images, only some of the regions are considered to be more important than the others (e.g., tumor in brain Magnetic Resonance Imaging (MRI)). This paper reviews the ap plication of ROI coding in the field of telemedicine. The image coding is done using Wavelet Transform (WT) based on Listless Speck (LSK). The Region of Interest (ROI) is obtained from user interaction and coded with the user given resolutio n to get high Compression Ratio (CR). In our propos ed method, instead of decompressing all the blocks, we decompress only th e similar blocks based on the index valued stored on the stack. Thus, our proposed method efficiently compresses the medical image. The performance measure can be analyzed by u sing Peak Signal to Noise Ratio (PSNR). The execution time of the pr oposed method will be reduced when compare to the o ther existing methods. The experimental result shows that the app lication of ROI coding using LSK brings about high compression rate and quality ROI.  

[1] Annadurai S. and Sundaresan M., Wavelet Based Color Image Compression using Vector Quantization and Morphology, in Proceedings of International Conference on Advances in Computing, Communication and Control , New York, USA pp. 391-396, 2009.

[2] Babu D. and Alamelu N., Wavelet Based Medical Image Compression using ROI EZW, International Journal of Recent Trends in Engineering , vol. 1, no. 3, pp. 97-100, 2009.

[3] Balraju M., Govardhan A., Chandra N., Basha S., and Srinivas P., Loss Controllable Image Compression for Color Images using Block Based Binary Plane Technique, International Journal of Engineering Studies , vol. 1, no. 1, pp. 59-70, 2009.

[4] Barnsley M., Fractals Everywhere , Second Edition, Elsevier, 1993.

[5] Barnsley M., Ervin V., Hardin D., and Lancaster J., Solution of an Inverse Problem for Fractals and Other Sets, National Academy of Sciences of the United States of America , vol. 83, no. 7, pp. 1975-1977, 1986.

[6] Bhardwaj A. and Ali R., Image Compression using Modified Fast Haar Wavelet Transform, World Applied Sciences Journal, World Applied Sciences Journal, World Applied Sciences Journal, vol. 7, no. 5, pp. 647-653, 2009.

[7] Bhat G., Baba A., Khan E., Efficient Image Compression Technique using Self Organizing Feature Maps, International Journal of Engineering Science and Technology , vol. 2, no. 12, pp. 7609-7615, 2010.

[8] Bhavani S., Performance Evaluation of Adaptive Mesh Based 3D MRI Compression using Wavelet Coding Schemes, International Journal of Engineering Science and Technology , vol. 2, no 6, pp. 2354-2358, 2010.

[9] Bovik Z. and Alan C., A Universal Image Quality Index, IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.

[10] Brian MRI images from

[11] El-Rube I., Fayed H., and El-Nahas S., Medical Endoscopic Image Coding: A Comparative Study, Canadian Journal on Image Processing and Computer Vision , vol. 1, no. 2, pp. 16-21, 2010.

[12] Fisher Y., Fractal Image Compression , Springer, New York, 1995.

[13] Ganguly D., Chakraborty S., and Kim T., A Cognitive Study on Medical Imaging, International Journal of BioBScience and BioB Technology , vol. 2, no. 3, pp. 1-18, 2010.

[14] Gaudeau Y. and Moureaux J., Lossy Compression of Volumetric Medical Images with 3D Dead-zone Lattice Vector Quantization, Annals of Telecommunications , vol. 64, no. 5-6, pp. 359-367, 2010.

[15] Ghrare S., Ali M., Jumari K., and Ismail M., An Efficient Low Complexity Lossless Coding Algorithm for Medical Images, American Journal of Applied Sciences , vol. 6, no. 8, pp. 1502-1508, 2009.

[16] Gray R. and Neuhoff D., Quantization, IEEE Transaction Information Theory , vol. 44, no. 6, 1998.

[17] Jacquin A., Fractal Image Coding: A Review, in Proceedings of IEEE , pp. 1451-1465, 1993.

[18] Kharate G, Ghatol A., and Rege P., Image Compression using Wavelet Packet Tree, ICGSTBGVIP Journal , vol. 5, no. 7, pp. 41-43, 2005.

[19] Kharate G. and Patil V., Color Image Compression Based On Wavelet Packet Best Tree, International Journal of Computer Science Issues , vol. 7, no. 2, pp. 31-35, 2010.

[20] Kil S., Lee J., Shen D., Ryu J., Lee E., Min H., and Hong S., Lossless Medical Image Compression using Redundancy Analysis, International Journal of Computer Science and 50 Network Security , vol. 6, no. 1A, pp. 50-56, 2006.

[21] Kivijarvia J., Ojala T., Kaukoranta T., Kuba A., Nyulb L., and Nevalainen O., A Comparison of Lossless Compression Methods for Medical Images, Computerized Medical Imaging and Graphics , vol. 22, no. 4, pp 323-39, 1998.

[22] Koh C. and Mukherjee J., New Efficient Methods of Image Compression in Digital Cameras with Color Filter Array, IEEE Transactions on Consumer Electronics , vol. 49, no. 4, pp. 1448-1456, 2003.

[23] Lam K., Lau W., and Li Z., The Effects on Image Classification using Image Compression Technique, available at: http: // www. isprs. org/ proceedings/ XXXIII/ congress/ part7/ 744_ XXXIII-part7.pdf, last visited 2000.

[24] Liu D., Sun X., and Wu F., Edge-Based in Painting and Texture Synthesis for Image Compression, in Proceedings of IEEE International Conference on Multimedia and Expo , Beijing, pp. 1443-1446, 2007.

[25] Palanisamy G. and Samukutti A., Medical Image Compression using a Novel Embedded Set Partitioning Significant and Zero Block Coding, The International Arab Journal of Informat ion Technology, Vol. 12, No. 3, May 2015 228 the International Arab Journal of Information Technology , vol. 5, no. 2, pp 132-139, 2008.

[26] Ramesh S. and Shanmugam A., Medical Image Compression using Wavelet Decomposition for Prediction Method, International Journal of Computer Science and Information Security , vol. 7, no. 1, pp. 262-265, 2010.

[27] Ruchika M. and Singh A., Compression of Medical Images using Wavelet Transforms, International Journal of Soft Computing and Engineering , vol. 2, no. 2, pp. 339-343, 2012.

[28] Satyanarayana B., Govardhan A., and Murthy H., A Novel Method of Shape Based Image Compression using Spectral Curvature Scaling, International Journal of Advanced Engineering and Application, vol. 1, pp. 203-206, 2010.

[29] Sousa C., Cavalcante A., Guilhon D., and Barros A., Image Compression by Redundancy Reduction, in Proceedings of the 7 th International Conference on Independent Component Analysis and Signal Separation , Springer Berlin Heidelberg, pp. 422-429, 2007.

[30] Sudha V. and Sudhakar R., Two Dimensional Medical Image Compression Techniques-A Survey, ICGSTBGVIP Journal , vol. 11, no. 1, pp. 9-20, 2011.

[31] Sumalatha R. and Subramanyam M., Region Based Coding of 3D Magnetic Resonance Images for Telemedicine Applications, International Journal of Computer Applications , vol. 5, no.12, pp. 1-3, 2010.

[32] Tamilarasi M. and Palanisamy V., An Efficient Embedded Coding For Medical Image Compression using Contourlet Transform, European Journal of Scientific Research , vol. 49, no. 3, pp. 442-454, 2011.

[33] Thakur N. and Kakde O., Color Image Compression with Modified Fractal Coding on Spiral Architecture, Journal Of Multimedia , vol. 2, no. 4, pp. 55-66, 2007.

[34] Vaquero J., Vilar R., Santos A., and Pozo F., Cardiac MR Imaging Compression: Comparison between Wavelet Based and JPEG Methods, in Proceedings of Computers in Cardiology , Vienna, Austria, pp. 657-660, 1995.

[35] Vidhya K. and Shenbagadevi S., A Two Component Medical Image Compression Technique, International Journal of Recent Trends in Engineering , vol. 1, no. 1, pp. 591- 593, 2009

[36] Walmsley N., Skodras A., and Curtis K., A Fast Picture Compression Technique, IEEE Transactions on Consumer Electronics , vol. 40, no. 1, pp. 11-19, 2002.

[37] Wang Z., Bovik A., Sheikh H., and Simoncelli E., Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600-612, 2004.

[38] Wu F. and Sun X., Image Compression by Visual Pattern Vector Quantization, in Proceedings of Data Compression Conference , Snowbird, pp. 123-131, 2008.

[39] Yang M., Trifas M., Chen L., Song L., Buenos- Aires D., and Elston J., Secure Patient Information and Privacy in Medical Imaging, Journal of Systemics , Cybernetics and Informatics, vol. 8, no. 3, pp 63-66, 2011.

[40] Zeybek E. and Nait-Ali A., Improvement of JPEG2000 Lossy Compression Performances using Preliminary Non-linear Filtering, International Journal of Information and Communication Engineering , vol. 4, no. 1, pp. 24-29, 2008.

[41] Zukoski M., Boult T., and Iyriboz T., A novel Approach for Medical Image Compression, International Journal Bioinformatics Research and Applications , vol. 2, no. 1, pp. 89-103, 2006. TMP Rajkumar received his BE degree in electronics engineering from SSIT Tumkur and MTech in industrial electronics from Karnataka Regional Engineering college (currently known as NITK) Surthkal. Presently he is perusing his PhD from JNTU Hyderabad and working as faculty in E and C Dept Anjuman Engineering College Bhatkal. He is Fellow member of Institution of Engineers (FI E) India, and also Fellow member of Electronics and Telecommunication Engineers (FIETE). Mrityunjaya Latte received his BE degree in electrical engineering and ME from SDM College of Engineering and Technology. Dharwad, Karnataka India. He was awarded the PhD degree in 2004 for his work in the area of digital Signal Processing. Presently he is working as Principal, J SS Academy of Technical Education, Bangalore. His research interests include coding, image processing and multiresolution transforms. He received a Best PAPER award for his paper in a National Conference NCSSS 2002 held at Coimbatore India MES.