Image query is a critical functional component of systems that integrate biomedical images with non-image data. Query tools are vital in order for them to find and retrieve information in all bioinformatics databases. Many biomedical repositories are accruing a wealth of images, such as the National Cancer Imaging Archive (NCIA) and the American College of Radiology Imaging Network (ACRIN), which are building image collections from diverse clinical trials. The current repositories provide the research community technologies to federate data archives, but techniques are needed to permit researchers to explore the various resources, pose questions, correlate image data with related non-image data, and formulate new hypotheses and research directions. There is an emerging need for intelligent image query tools to enable users to search the image resources in an intuitive way. Our goal is to create image query tools to help users create queries that exploit the capability of biomedical ontologies to enable search for images that are annotated using these knowledge sources.
In the IQ project, we will develop semantic methods for searching for annotated images. We will address these challenges: (1) Complexity if image content and semantics, (1) Relating radiology imaging to other non-imaging data, and (3) Terminology challenges of synonymy and polysemy. We will address these challenges by creating an ontology to support image query. An ontology is an explicit knowledge representation that specifies the entities and relations among those entities in a domain in a human-readable and machine-processable format. We will create methods to permit users to search for images based ontology terms, and the ontologies will also be used to expand user queries. We will also develop an intuitive interface to accessing the ontologies and composing queries.