The systematic combination of anatomy and nomenclature is an essential component of all computer processes that seek to visualize, relate, manipulate, and compare three-dimensional human anatomic structures. Structure names must be elemental, categorized and associated with standard comparison images in order to: create computer visualizations at multiple levels of detail; associate structures spatially and symbolically; assign anatomic units their physical characteristics; and categorize new patient- derived anatomic data. This project will demonstrate that the Systematic Combination of Anatomy and Nomenclature (SCAN) can enable semi-automatic identification and segmentation of structures in radiological data. The first long-term goal of this project is to relate three-dimensional anatomic knowledge in the Visible Human Data (VHD, a full digital description of the human being) to the symbolic anatomic knowledge in the Systemized Nomenclature of Human and Veterinary Medicine (SNOMED, a data table relating terms to codes), which is a component of the Unified Medical Language System (UMLS, a relational database). This project will identify elemental gross anatomic structures within the nomenclature of SNOMED and relate them to registered, segmented slice image sets and high quality Virtual Reality Modeling Language (VRML) surface models derived from VHD. The second long-term goal of this project is to link the standard anatomic knowledge to new three-dimensional radiological data for any patient. This project will generate a semi-automated process for combination of patient- specific anatomic data and nomenclature using mutual information for automatic multimodality image fusion (MIAMI Fuse, a process which integrates three- dimensional images of different modalities into a single image set) to link new data to standardized data and subsequently to nomenclature. By integrating standardized three-dimensional and symbolic anatomy, coupled with semi-automated fusion of new radiological data to these standards, SCAN will provide the foundation for widespread advances in biomedical visualization, simulation, medical education, diagnostics, and treatment planning.