Journal of Computer Science 9 (9): 1181-1189, 2013 ISSN: 1549-3636 © 2013 Science Publications doi:10.3844/jcssp.2013.1181.1189 Published Online 9 (9) 2013 (http://www.thescipub.com/jcs.toc) Corresponding Author: Sridevi, S., Department of Computer Science and Engineering, Sethu Insitute of Technology, Kariapatti, Tamil Nadu, India 1181 Science Publications JCS MEDICAL IMAGE COMPRESSION TECHNIQUE USING LISTLESS SET PARTITIONING IN HIERARCHICAL TREES AND CONTEXTUAL VECTOR QUANTIZATION FOR BRAIN IMAGES 1 Sridevi, S. and 2 V.R. Vijayakumar 1 Department of Computer Science and Engineering, Sethu Insitute of Technology, Kariapatti, Tamil Nadu, India 2 Department of Electronics and Communication Engineering, Anna University, Coimbatore, Tamil Nadu, India Received 2013-07-06, Revised 2013-07-24; Accepted 2013-08-03 ABSTRACT A hybrid image compression techniques has been developed to compress medical images. Due to the extensive use of medical images like CT and MR scan, these medical imagery are stored for a longer period for the continuous monitoring of the patients and the amount of data associated with images is large and it occupies enormous storage capacity. So, the medical images need to be compressed to reduce the storage cost and for transmission without any loss. In this study, a hybrid method which combines the Listless Set Partitioning in Hierarchical Trees (LSPIHT) and the Contextual Vector Quantization (CVQ) method for the compression of brain images. Here, the region containing the most important information for diagnosis is called Region of Interest (ROI) and this is to be compressed with out any loss in the quality. In this method, the ROI is encoded separately using LSPIHT and the Back Ground region (BG) is encoded using CVQ. Finally, the two regions are merged together to get the reconstructed image. Our results show that the proposed method gives very good image quality for diagnosis without any degradable loss. The performance of the compression technique is evaluated using the parameters (CR, MSE, PSNR) and achieved better result compared to other existing methods. As a result, we strongly believe that using our method, we can overcome the limitations in storage and transmission of medical images that are produced day by day. Keywords: Lspiht, CVQ, ROI, BG, MSE, CR, PSNR 1. INTRODUCTION Image compression techniques play a major in all areas, especially in medical image domain. The medical images are stored for a longer time and it has to be transmitted to other location like from one hospital to another hospital for reference. To store and transmit these medical images, we need enormous amount of storage capacity and bandwidth. Due to the limitations of storage and transmission, the medical images are to be compressed. Normally, compression can be classified into two major categories. (1) Lossy compression and (2) Lossless compression. In lossy compression, there will be a small distortion in the image with higher compression rate. But in the case of lossless, the image is compressed without any loss in the quality. Recent study shows that there has been great interest in lossy compression of medical images. Here the loss in data are very minimal and there wont be any degradable loss in the quality of image for diagnosis by the radiologists. Nowadays, lossy compression techniques are combined with scalar or vector quantization for efficient compression result. Many compression algorithms (Gonzalez and Woods, 2009) produce high compression rates with affordable loss of quality but physician may not accept any loss in diagnostically important regions of images. The region which contains the more important information