Content-based image retrieval in radiology: current status and future directions
Open accessAbstract
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.
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Analysis of exams that produce hundreds of images, such as CT and MRI, poses challenges even for experienced specialists.
“Radiological imaging can be extremely complex, and it is recognized that the analysis of exams that produce hundreds of images, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses challenges, even for experienced specialists(1-3).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [2]
Texture features have become particularly important because they can reflect details of a lesion identified in an image.
“Texture features have become particularly important because they can reflect the details of a lesion identified in an image(1,2).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [2]
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References 82 · Cited by 388
Research works printed but not linked: 2
- Doyle S, et al: Using manifold learning for content-based image retrieval of prostate histopathology. in MICCAI 2007 Workshop on Content-Based Image Retrieval for Biomedical Image Archives. Brisbane, Australia, 2007
- Chin Y, et al: An automatic liver segmentation initiali- zation information retrieval strategy for a CBIR followed by a new liver volume segmentation method for CT and MRI image datasets. in MICCAI 2007 Workshop on Content-based Image Retrieval for Biomedical Image Archives. 2007. Brisbane, Australia
- Authors
- Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B.
- Affiliations
- Boğaziçi University (TR)
- Stanford Medicine (US)
- Tel Aviv University (IL)
- Corresponding author
- Acar, Burak
- Venue
- Journal of Digital Imaging
- Published
- 2010-04-08
- Volume
- 24
- Issue
- 2
- Pages
- 208-222
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC3056970
- OpenAlex
- W2052271957
- Cited by
- 388
- References
- 82
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 10 · 2023: 21 · 2022: 14 · 2021: 13 · 2020: 26 · 2019: 30 · 2018: 27 · 2017: 29 · 2016: 46 · 2015: 38 · 2014: 44 · 2013: 39 · 2012: 24
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (104E035)
- National Institutes of Health
- National Cancer Institute (R01CA072023, CA72023)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- content-based image retrieval, radiology, decision support, image metadata, pixel-based features, image archives
- MeSH terms
- Database Management Systems, Forecasting, Humans, Pattern Recognition, Automated, Radiographic Image Interpretation, Computer-Assisted, Radiology Information Systems, Information Storage and Retrieval
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