Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

TL;DR

This paper discusses how Content-Based Image Retrieval (CBIR) enhances remote clinical diagnosis and healthcare by enabling efficient image retrieval, supporting real-time decision-making, and improving medical data management.

Contribution

It highlights the importance of CBIR in healthcare, discusses necessary infrastructure, and recommends standards and architectures for effective implementation.

Findings

CBIR improves diagnostic accuracy and efficiency.

Standardization and efficient transmission are crucial for healthcare CBIR.

CBIR supports real-time remote diagnosis and patient monitoring.

Abstract

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.