Concept advances content- based image retrieval
Promising new technologies such as content-based image retrieval can enrich medical informatics. Yet this technology is not well established in PACS due to its inability to bridge the divide between low-level computational pixel analysis and high-level human cognitive capabilities.
Promising new technologies such as content-based image retrieval can enrich medical informatics. Yet this technology is not well established in PACS due to its inability to bridge the divide between low-level computational pixel analysis and high-level human cognitive capabilities. Researchers at the Aachen University of Technology in Germany have proposed a nomenclature and classification scheme for analysis and assessment of medical CBIR systems. The scheme was published in the Journal of Digital Imaging in February (Epub ahead of print).
CBIR enables image access by image pattern rather than by alphanumeric-based indices. Thomas M. Deserno, Ph.D., of the department of medical informatics at Aachen, attempted to address the core features and required functionality of medical CBIR systematically, using the concept of gaps as a unifying idea to highlight potential shortcomings. Since an evaluation of all systems is not practical, an ontology of gaps provides categories and classes of systems, he said.
The literature identifies two gaps in CBIR techniques and defines an ontology of 14 gaps that address image content and features, as well as system performance and usability. There is a highly significant gap, for instance, in the level of integration of CBIR into general patient care information systems. Another occurs in the automation of feature extraction, Deserno said.
