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 inJournal of physics. Conference series Vol. 1019; no. 1; pp. 12044 - 12055
Main Authors Mohd Zin, Nor Asma, Yusof, Rozianiwati, Lashari, Saima Anwar, Mustapha, Aida, Senan, Norhalina, Ibrahim, Rosziati
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.06.2018
Subjects
Algorithms
Feature extraction
Image management
Image retrieval
Low level
Machine learning
Medical imaging
Physics
Semantics
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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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1019/1/012044