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Proceedings Paper

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules
Author(s): Ye Xu; Michael C. Lee; Lilla Boroczky; Aaron D. Cann; Alain C. Borczuk; Steven M. Kawut; Charles A. Powell
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Paper Abstract

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

Paper Details

Date Published: 3 March 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600Z (3 March 2009); doi: 10.1117/12.807866
Show Author Affiliations
Ye Xu, Philips Research North America (United States)
Michael C. Lee, Philips Research North America (United States)
Lilla Boroczky, Philips Research North America (United States)
Aaron D. Cann, Columbia Univ. (United States)
Alain C. Borczuk, Columbia Univ. (United States)
Steven M. Kawut, Columbia Univ. (United States)
Charles A. Powell, Columbia Univ. (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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