Content-based image retrieval in radiology: Current status and future directions

Ceyhun Burak Akgül, Daniel L. Rubin, Sandy Napel, Christopher F. Beaulieu, Hayit Greenspan, Burak Acar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

319 Scopus citations

Abstract

Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata- based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

Original languageEnglish
Pages (from-to)208-222
Number of pages15
JournalJournal of Digital Imaging
Volume24
Issue number2
DOIs
StatePublished - Apr 2011

Funding

FundersFunder number
TÜBİTAK104E035
National Institutes of Health
National Cancer InstituteR01CA072023

    Keywords

    • Content-based image retrieval
    • Decision support
    • Digital image management
    • Imaging informatics
    • Information storage and retrieval

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