ISSN: 2278 – 7798 International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 2, February 2013 501 All Rights Reserved © 2013 IJSETR A SURVEY ON VARIOUS MEDICAL IMAGE COMPRESSION TECHNIQUES ABSTRACT The need for an efficient technique for compression of Images ever increasing because the transmission & storage. Telemedicine characterized by transmission of medical data and images between users is one of the emerging fields in medicine.Huge bandwidth is necessary for transmitting medical images over the internet. Resolution factor and number of images per diagnosis makes even the size of the images that belongs to a single patient to be very large in size. So there is an immense need for efficient compression techniques for use in compressing these medical images. Each ofthe regions that are considered to be more important than others in medical images is termed as a Region of Interest (ROI)e.g. tumor region of the brain MRI. Thus, the regions of interest can be coded with high spatial resolution than the background while transmitting the images. By this, ROI of high compression rate and high quality can be obtained. This paper reviews the application of ROI coding in the field of telemedicine. ROI coding with high spatial resolution than the background is accomplished using tiling method. High compression ratio is achieved by obtaining the ROI through user interaction and coding with the user given resolution. The experimental result shows that the application of ROI coding achieves high compression rate and quality ROI.This paper outlines the comparison of compression methods such as JPEG-LS and Interframe Coding, Optimized Volume of Interest Coding, Motion Compensation and Customized Entropy Coding, EZW Encoding with Huffman Encoder, Curvelet Transform, Visually lossless compression, Simple Selective Scan order with Bit Plane Slicing on the basis of compression ratio and compression quality. Keywords:—Image compression, Telemedicine, Region of Interest (ROI), Huffman codes, Huffman encoding, Huffman decoding, symbol, source reduction ,JPEG-LS, Pipeline, FMRI, EZW, Curvelet Transform, Predictive Compression, Hybrid Compression INTRODUCTION: Two ways of classifying compression techniques are mentioned here: (a) Lossless vs. Lossy compression: In lossless compression schemes, the reconstructed image, after compression, is numerically identical to the original image. However lossless compression can only a achieve a modest amount of compression. An image reconstructed following lossy compression contains degradation relative to the original. Often this is because the compression scheme completely discards redundant information. However, lossy schemes are capable of achieving much higher compression. Under normal viewing conditions, no visible loss is perceived (visually lossless). (b) Predictive vs. Transform coding: In predictive coding, information already sent or available is used to predict future values, and the difference is coded. Since this is done in the image or spatial domain, it is relatively simple to implement and is readily adapted to local image characteristics. Differential Pulse Code Modulation (DPCM) is one particular example of predictive coding . Transform coding, on the other hand, first transforms the image from its spatial domain representation to a different type of representation using some well-known transform and then codes the transformed values (coefficients). This method provides greater data compression compared to predictive methods, although at the expense of greater computation. Neelesh Kumar Sahu Asst. Professor, Faculty of Engineering Shri Shankaracharya Group of Institution, Bhilai, India Chandrashekhar Kamargaonkar Associate Professor, Faculty of Engineering Shri Shankaracharya Group of Institution, Bhilai, India