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Proceedings Paper

A context-aware approach to content-based image retrieval of lung nodules
Author(s): Jacob V. Gardner; Daniela Raicu; Jacob Furst
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Paper Abstract

We are investigating various techniques to improve the quality of Content-Based Image Retrieval(CBIR) for computed-tomography(CT) scans of lung nodules. Previous works have used linear regression models1 and artificial neural networks(ANN)6 to predict the similarity between two nodules. This paper expands upon this work incorporating contextual information around lung nodules to determine if the existing model using an ANN will produce a better correlation between content-based and semantic-based human perceived similarity.

Paper Details

Date Published: 9 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632I (9 March 2011); doi: 10.1117/12.878400
Show Author Affiliations
Jacob V. Gardner, Missouri Univ. of Science and Technology (United States)
Daniela Raicu, DePaul Univ. (United States)
Jacob Furst, DePaul Univ. (United States)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers M.D.; Bram van Ginneken, Editor(s)

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