Content-based image retrieval in radiology: current status and future directions.
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.
Akgül
Ceyhun Burak
CB
Electrical and Electronics Engineering Department, Volumetric Analysis and Visualization Lab., Boğaziçi University, Istanbul, Turkey.
Rubin
Daniel L
DL
Napel
Sandy
S
Beaulieu
Christopher F
CF
Greenspan
Hayit
H
Acar
Burak
B
D003628:Database Management Systems / Q000639:trends; D005544:Forecasting; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000639:trends; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011873:Radiology Information Systems / Q000639:trends*