Significant new knowledge about human behavior and the brain has come to light in recent years, due in part to rapid technical developments in imaging. As the role of imaging becomes increasingly important in neurosciences, effective methods for managing and retrieving images will become even more critical; without such advances, further progress will be hindered. The goal of this proposal is the automated summarization of large imaging sets. Image summarization proffers a method to compress imaging studies by selecting only pertinent image slices that objectively document a patient's condition; as such, its applications include multimedia electronic medical records, telemedicine, and teaching files. In Phase I, development is focused on a customizable brain atlas used for registering patient imaging studies in order to select key images. This phase addresses selection of images from "normal" studies and studies with only subtle morphological changes, as typical of most patients with psychiatric disorders. Automatic techniques for customizing the atlas to imaging study acquisition parameters are developed, in addition to registration methods for mapping the atlas to the patient's original study. Building from this initial work, Phase II expands to encompass selection of images from "abnormal" studies that exhibit gross morphological changes through principle component analysis, further customization of the atlas for different age groups (e.g., pediatric), and incorporation of structured data entry (SDE) and natural language processing (NLP) of medical reports to help guide automatic selection of key images. The resultant product will be a fully automated software system that can select relevant images from any imaging study. Initial evaluation in Phase I will examine the performance of the contrast customizable atlas and summarization/relevant slice selection, as compared to human experts. [unreadable] [unreadable]