Context based medical image coding with contextual set partitioning with improved active contours algorithm

Image compression plays a crucial role in medical imaging, allowing efficient manipulation, storage, and transmission. Nevertheless, in medical applications the need to conserve the diagnostic validity of the image requires the use of lossless compression methods, producing low compression factors. In this paper, a novel near-lossless compression scheme for context based coding is proposed here and yields significantly better compression rates. In this proposed method the object and the background are obtain using improved active contours with selective local or global segmentation image segmentation, and the contextual part of the image is encoded selectively on the high priority basis with a very low compression rate (high bpp) and the background of the image is separately encoded with a low priority and a high compression rate (low bpp) and they are recombined for the reconstruction of the image. As a result, high over all compression rates, better diagnostic image quality and improved performance parameters are obtained. The algorithm is tested on experimental medical images from different modalities and different body districts and results are reported.