Ontology-based mammography annotation and Case-based Retrieval of breast masses
Expert Systems with Applications: An International Journal
Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring
Computers in Biology and Medicine
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The Content-based Image Retrieval (CBIR) methods have been applied to medical field to aid diagnosis and other medical processes for a relatively long time. This type of systems, with the growth of digital medical image databases, sometimes does not provide information tailored to the end user requirements. Typically there is a semantic gap between the low-level features extracted form the images and high-level concepts required by the user. This paper proposes an ontology-based search and retrieval as a supplementary method to associate high- level concepts and semantics to medical image data and exploit these semantic media for information search and retrieval. The main goal is to perform a search on a medical image repository and to retrieve relevant medical images and information to a particular case in order to assist diagnosis process. We demonstrate the functionality of the system in case of a mammography imaging database. The system uses an ontology-based search and retrieval for the medical data as a complementary solution to provide more efficient and insight access to the stored data. Keywords: Ontology-based Search, Semantic Annotation, Medical Image Retrieval, Semantic Web