MEDICAL IMAGE COMPRESSION TECHNIQUE USING LISTLESS SET PARTITIONING IN HIERARCHICAL TREES AND CONTEXTUAL VECTOR QUANTIZATION FOR BRAIN IMAGES

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 th e continuous monitoring of the patients and the amount of data associated with images is large and it oc cupies 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 hyb rid method which combines the Listless Set Partitio ning 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 Regio n of Interest (ROI) and this is to be compressed with ou t 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 ve ry good image quality for diagnosis without any degrad able loss. The performance of the compression techn ique is evaluated using the parameters (CR, MSE, PSNR) and achieved better result compared to other existin g methods. As a result, we strongly believe that usin g our method, we can overcome the limitations in st orage and transmission of medical images that are produce d day by day.