Content-Based Image Retrieval in Medical Domain: A Review

Content-based Image Retrieval (CBIR) aids radiologist to identify similar medical images in recalling previous cases during diagnosis. Although several algorithms have been introduced to extract the content of the medical images, the process is still a challenge due to the nature of the feature itse...

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Bibliographic Details
Published in:Journal of Physics: Conference Series
Main Author: Mohd Zin N.A.; Yusof R.; Lashari S.A.; Mustapha A.; Senan N.; Ibrahim R.
Format: Conference paper
Language:English
Published: Institute of Physics Publishing 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049922855&doi=10.1088%2f1742-6596%2f1019%2f1%2f012044&partnerID=40&md5=35b54678f57991f23f54fe6cc422be63
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Summary:Content-based Image Retrieval (CBIR) aids radiologist to identify similar medical images in recalling previous cases during diagnosis. Although several algorithms have been introduced to extract the content of the medical images, the process is still a challenge due to the nature of the feature itself where most of them are extracted in low level form. In addition to the dimensionality reduction problem caused by the low-level features, current features are also insufficient to convey the semantic meaning of the images. This paper reviews the recent works in CBIR that attempts to reduce the semantic gap in extracting the features from medical images, precisely for mammogram images. Approaches such as the use of relevance feedback, ontology as well as machine learning algorithms are summarized and discussed. © Published under licence by IOP Publishing Ltd.
ISSN:17426588
DOI:10.1088/1742-6596/1019/1/012044