Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare
This is an incremental review that outlines the potential benefits and challenges of CBIR for clinicians and healthcare systems in remote settings.
The paper addresses the application of Content-Based Image Retrieval (CBIR) in remote clinical diagnosis and healthcare, highlighting its role in improving evidence-based diagnosis, administration, teaching, and research by facilitating visual and automatic decision-making in real-time consultations and patient surveillance.
Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.