Contributions to HEVC Prediction for Medical Image Compression

Abstract

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC.