OPTIMUM DCT COMPRESSION OF MEDICAL IMAGES USING NEURAL NETWORKS

Adnan Khashman, Kamil Dimililer

Abstract

Medical imaging requires storage of large quantities of digitized data Efficient storage and transmission of medical images in telemedicine is of utmost importance however. Due to the constrained bandwidth and storage capacity, a medical image must be compressed before transmission or storage. An ideal image compression system must yield high quality compressed images with high compression ratio; this can be achieved using DCT-based image compression, however the contents of the image affects the choice of an optimum compression ratio. In this paper, a neural network is trained to relate the x-ray image contents to their optimum compression ratio. Once trained, the optimum DCT compression ratio of the x-ray image can be chosen upon presenting the image to the network. Experimental results suggest that out proposed system, can be efficiently used to compress x-rays while maintaining high image quality.

References

  1. Ashraf, R., Akbar, M., 2005. Absolutely lossless compression of medical images. In EMBS 2005, Proceedings of IEEE 27th Conference on Engineering in Medicine and Biology, 4006-4009.
  2. Ashraf, R., Akbar, M., 2006. Adaptive Architecture Neural Nets for Medical Image Compression. In ICCIMA 2007, IEEE International Conference on Engineering of Intelligent Systems, 1-4.
  3. Ciernak, R., 2004. Image Compression algorithm based on neural networks. In Lecture Notes in Artificial Intelligence. Vol. 3070, Springer-Verlag, Berlin Heidelberg, 706-711.
  4. Chikouche, D., Benzid, R., Bentoumi M., 2008. Application of the DCT and Arithmetic Coding to Medical Image Compression. In ICTTA 2008, 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 1-5.
  5. Dokur, Z., 2008. A unified framework for image compression and segmentation by using an incremental neural network. In An International Journal on Expert Systems with Applications, 34, 611- 619.
  6. Khashman, A., Dimililer, K., 2005. Comparison Criteria for Optimum Image Compression, In EUROCON'05, Proceeding of IEEE International Conference, 935- 938.
  7. Khashman, A., Dimililer, K., 2007. Neural Networks Arbitration for Optimum DCT Image Compression. In EUROCON'07, Proceeding of IEEE International Conference, 151-156.
  8. Liying, M., Khashayar, K., 2005. Adaptive Constructive Neural Networks using Hermite Polynomials for Image Compression. In Lecture Notes in Computer Science, Vol. 3497, Springer-Verlag, Berlin Heidelberg, 713-722.
  9. Meyer-Base, A., Jancke, K., Wismuller, A., Foo, S., Martientz, T., 2005. Medical image compression using topology-preserving neural networks. In Engineering Applications of Artificial Intelligence, 18(4), 383-392.
  10. Nadenau, M.J., Reichel, J., Kunt, M., 2003. Wavelet Based Color Image Compression: Exploiting the Contrast Sensitivity Function. In IEEE Trans. Image Processing, 12(1), 58-70.
  11. Northan, B., Dony, R.D., 2006. Image Compression with a multiresolution neural network. In Canadian Journal of Electrical and Computer Engineering. 31(1), 49-58.
  12. Raj, N.P., Venkateswarlu, T., 2007. A Novel Approach to Medical Image Compression using Sequential 3D DCT. In ICCIMA 2007, International Conference on Computational Intelligence and Multimedia Applications, 3, 146-152.
  13. Ratakonda, K., Ahuja, N., 2002. Lossless Image Compression with Multiscale Segmentation. In IEEE Trans. Image Processing, 11(11), 1228-1237.
  14. Shih, F.Y., Wu, Y., 2005. Robust watermarking and compression for medical images based on genetic algorithms. In An International Journal on Information Sciences, 175, 200-216.
  15. Singh, S., Kumar, V., Verna, H.K., 2007. Adaptive Threshold-based block classification in image compression for teleradiology. In Computers in Biology and Medicine, 37, 811-819.
  16. Soliman, H.S., Omari, M., 2006. A neural networks approach to image compression. In Journal of Applied Soft Computing, 6(3), 258-271.
  17. Veisi, S., Jamzad, M., 2007. Image Compression with Neural Networks Using Complexity Level of Images. In ISPA 2007, Proceedings of the 5th IEEE International Symposium on image and Signal Processing and Analysis, 282-287.
  18. Vilovic, I., 2006. An Experience in Image Compression Using Neural Networks. In ELMAR-2006, Proceedings of IEEE 48th International Symposium on Multimedia Signal Processing and Communications, 95-98.
  19. Zukoski, M.J., Bould, T., Iyriboz, T., 2006. A Novel Approach to Medical Image Compression. In International Journal on Bioinformatics Research and Applications, 2(1), 89-103.
Download


Paper Citation


in Harvard Style

Khashman A. and Dimililer K. (2009). OPTIMUM DCT COMPRESSION OF MEDICAL IMAGES USING NEURAL NETWORKS . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 91-96. DOI: 10.5220/0001865600910096


in Bibtex Style

@conference{iceis09,
author={Adnan Khashman and Kamil Dimililer},
title={OPTIMUM DCT COMPRESSION OF MEDICAL IMAGES USING NEURAL NETWORKS },
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={91-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001865600910096},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - OPTIMUM DCT COMPRESSION OF MEDICAL IMAGES USING NEURAL NETWORKS
SN - 978-989-8111-85-2
AU - Khashman A.
AU - Dimililer K.
PY - 2009
SP - 91
EP - 96
DO - 10.5220/0001865600910096