Medical imaging refers to the techniques and processes used to create images of the human body in digital form for medical procedures, seeking to reveal, diagnose or examine diseases. Medical images need large storage requirements when high resolution is demanded. Image compression plays a key role in telematics applications, especially in telemedicine so as to achieve a low bit rate for transmission or storage, while maintaining image information. For most medical images, the diagnostically significant information is localized over relatively small regions of interest (ROI). In this paper, an efficient coding and compression scheme is proposed which will compress the Region of Interest (ROI) more precisely and very high rate of lossy compression in the other regions of less importance. This algorithm is mainly based on Vector quantization, which is a powerful technique to compress a data sequence, such as speech or image, resulting in some loss of information. The given image is first divided (segmented) into region of interest (diagnostically important region) and region of less importance. This paper discusses and compares three region based compression methods applied to MRI and CT images. These techniques can be used in the detection of tumors. Simulation results indicate that good reconstruction quality is obtained in diagnostically important region and also high compression rate is ensured in other regions.
