Data from University of Paris Provide New Insights into Biomedical Informatics (A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations)
By a News Reporter-Staff News Editor at Biotech Week -- Researchers detail new data in Biotechnology. According to news reporting out of Paris, France, by NewsRx editors, research stated, "Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances."
Our news journalists obtained a quote from the research from the University of Paris, "Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest(ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost."
According to the news editors, the research concluded: "The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification."
For more information on this research see: A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. Journal of Biomedical Informatics, 2014;49():227-244. Journal of Biomedical Informatics can be contacted at: Academic Press Inc Elsevier Science, 525 B St, Ste 1900, San Diego, CA 92101-4495, USA. (Elsevier - www.elsevier.com; Journal of Biomedical Informatics - www.elsevier.com/wps/product/cws_home/622857)
Our news journalists report that additional information may be obtained by contacting C. Kurtz, University of Paris, LIPADE EA 2517, F-75006 Paris, France. Additional authors for this research include C.F. Beaulieu, S. Napel and D.L. Rubin (see also Biotechnology).
Keywords for this news article include: Paris, France, Europe, Biotechnology
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