PERFORMANCE EVALUATION OF IMAGE COMPRESSION FOR MEDICAL IMAGE

Most hospitals store medical image data in digital form using picture archiving and communication systems due to extensive data digitization of data and increasing telemedicine use. Data storage needs and bandwidth requirements have necessitated use of lossy compression techniques. Wavelet transforms successful use in image compression was extensively studied in literature. Image segmentation aims to partition an image domain into many mutually exclusive subdomains over which some image properties are homogeneous. In medical images, an object represents a diseased organ called a region of interest (ROI). Though literature suggested many procedures ROI are efficiently segmented through active contour use. ROI are compressed with lossless compression to maintain medical image integrity. This paper investigates Biorthogonal spline waveletperformance as a possible mode for medical image compression. Active Contour segments ROI in medical images. The performance of Set Partitioning in Hierarchical Trees (SPHIT) and Embedded zerotree wavelet algorithm (EZW) to compress images is evaluated.