Affiliation:
1. Anna University Chennai, India
Abstract
Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.
Reference54 articles.
1. Discrete Cosine Transform
2. Barnsley, M. F. & Sloan, A. D. (1988). A better way to compress images. Byte, 215-223.
3. Boliek, M., Gormish, M. J., Schwartz, E. L., & Keith, A. (1997). Next generation image compression and manipulation using CREW. Proceeding of IEEE ICIP, 3, 567-572.
4. Buccigrossi, R., & Simoncelli, E. P. (1997). EPWIC: Embedded predictive wavelet image coder. In Proceedings of 4th IEEE International Conference on Image Processing, (pp. 640-648). Santa Barbara, CA: IEEE Press.
5. Calderbank, R., Daubechies, I., Sweldens, W., & Yeo, B.-L. (1998). Wavelet transforms that map integers to integers. Journal of Applied and Computational Harmonic Analysis, (5), 332-369.