A semantic framework for the retrieval of similar radiological images based on medical annotations

Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Kurtz, Camille (University Paris Descartes, France) ; Napel, Sandy (Stanford University School of Medicine, USA) ; Beaulieu, Christopher (Stanford University School of Medicine, USA) ; Rubin, Daniel (Stanford University School of Medicine, USA)

Image retrieval approaches can assist radiologists by finding similar images in databases as a means to providing decision support. In general, images are indexed using low-level imaging features, and a distance function is used to find the best matches in the feature space. However, using low-level features to capture the appearance of diseases in images is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. We present a semantic framework that enables retrieving similar images based on high-level semantic image annotations. This framework relies on (1) an automatic approach to predict the annotations as semantic terms from Riesz texture image features and (2) a distance function to compare images considering both texture-based and radiodensity-based similarities among image annotations. Experiments performed on CT images emphasize the relevance of this framework.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Autres
Publisher:
Paris, France, 27-30 October 2015
Date:
Paris, France
27-30 October 2015
2014
Pagination:
5 p.
Published in:
Proceedings of the 21st IEEE International Conference on Image Processing (ICIP) 2014
DOI:
ISSN:
978-1-4799-5751-4
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2015-11-20, last modified 2019-06-11

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