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Proceedings Paper

A model for the relationship between semantic and content based similarity using LIDC
Author(s): Grace M. Dasovich; Robert Kim; Daniela S. Raicu; Jacob D. Furst
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Paper Abstract

There is considerable research in the field of content-based image retrieval (CBIR); however, few of the current systems incorporate radiologists' visual impression of image similarity. Our objective is to bridge the semantic gap between radiologists' ratings and image features. We have been developing a conceptual-based similarity model derived from content-based similarity to improve CBIR. Previous work in our lab reduced the Lung Image Database Consortium (LIDC) data set into a selection of 149 images of unique nodules, each containing nine semantic ratings by four radiologists and 64 computed image features. After evaluating the similarity measures for both content-based and semantic-based features, we selected 116 nodule pairs with a high correlation between both similarities. These pairs were used to generate a linear regression model that predicts semantic similarity with content similarity input with an R2 value of 0.871. The characteristics and features of nodules that were used for the model were also investigated.

Paper Details

Date Published: 9 March 2010
PDF: 10 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762431 (9 March 2010);
Show Author Affiliations
Grace M. Dasovich, Northwestern Univ. (United States)
Robert Kim, The Johns Hopkins Univ. (United States)
Daniela S. Raicu, DePaul Univ. (United States)
Jacob D. Furst, DePaul Univ. (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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