Description

Medical image compression is one of the growing research fields in biomedical applications. Mostly medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in healthcare settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. Resolution and quality scalability, on the other hand, refers to the ability to decode the compressed image at different resolution and quality levels, respectively. Thus we propose a new technique on compression based on the medical images. We segment the image into foreground and background image and perform lossy compression for background and lossless compression.