Contents 1. INTRODUCTION
The field of biological imaging is expanding quickly. This trend stems, in part, from recent developments in imaging, the advent of new medical imaging modalities and the increased performance of many existing ones. This expansion also results from new scientific and biological applications for such data, which are more concerned with mapping and statistical characterization rather than the conventional clinical paradigms of detection and diagnosis. The quantitative analysis of biological images is now often applied to populations of subjects in order to test scientific hypotheses. Despite the extensive use of computers in the biosciences, there is still a relatively low level of interaction between the research leaders in biomedicine and those in computational science. As a result, computer technology tends to lag behind the needs of the biosciences. At the same time, most bioscientists do not have the expertise required to create cutting edge computational software. In response to the need for new computational tools and for increased interaction between computational scientists and bioscientists, as well as other priorities identified by the National Institutes of Health (NIH) underscoring the essential and central role of computing in biomedical research [1], the Scientific Computing and Imaging Institute at the University of Utah has created the Center for Integrative Biomedical Computing (CIBC). Funded by the NIH National Center for Research Resources, our mission at the CIBC is to produce high performance open-source software for use in the biomedical research community, with a focus on image analysis, multi-scale tissue modeling and simulation, and scientific visualization.
