Image processing in radiology

Abstract

Medical imaging processing and analysis methods have significantly improved during recent years and are now being increasingly used in clinical applications. Preprocessing algorithms are used to influence image contrast and noise. Three-dimensional visualization techniques including volume rendering and virtual endoscopy are increasingly available to evaluate sectional imaging data sets. Registration techniques have been developed to merge different examination modalities. Structures of interest can be extracted from the image data sets by various segmentation methods. Segmented structures are used for automated quantification analysis as well as for three-dimensional therapy planning, simulation and intervention guidance, including medical modelling, virtual reality environments, surgical robots and navigation systems. These newly developed methods require specialized skills for the production and postprocessing of radiological imaging data as well as new definitions of the roles of the traditional specialties. The aim of this article is to give an overview of the state-of-the-art of medical imaging processing methods, practical implications for the radiologist's daily work and future aspects.