PUB - Publikationen an der Universität Bielefeld

Due to the rapid progress in medical imaging technology, analysis of multivariate image data is receiving increased interest. However, their visual exploration is a challenging task since it requires the integration of information from many different sources which usually cannot be perceived at once by an observer. Image fusion techniques are commonly used to obtain information from multivariate image data, while psychophysical aspects of data visualization are usually not considered. Visualization is typically achieved by means of device derived color scales. With respect to psychophysical aspects of visualization, more sophisticated color mapping techniques based on device independent (and perceptually uniform) color spaces like CIELUV have been proposed. Nevertheless, the benefit of these techniques is limited by the fact that they require complex color space transformations to account for device characteristics and viewing conditions. In this paper we present a new framework for the visualization of multivariate image data using image fusion and color mapping techniques. In order to overcome problems of consistent image presentations and color space transformations, we propose perceptually optimized color scales based on CIELUV in combination with sRGB (IEC 61966-2-1) color specification. In contrast to color definitions based purely on CIELUV, sRGB data can be used directly under reasonable conditions, without complex transformations and additional information. In the experimental section we demonstrate the advantages of our approach in an application of these techniques to the visualization of DCE-MRI images from breast cancer research.