Concept advances content- based image retrieval

Article

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.

Recent Videos
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
What New Interventional Radiology Research Reveals About Treatment for Breast Cancer Liver Metastases
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
What New Brain MRI Research Reveals About Cannabis Use and Working Memory Tasks
Current and Emerging Legislative Priorities for Radiology in 2025
How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2
Related Content
© 2025 MJH Life Sciences

All rights reserved.