Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

Ye Xu; Michael C. Lee; Lilla Boroczky; Aaron D. Cann; Alain C. Borczuk; Steven M. Kawut; Charles A. Powell

doi:10.1117/12.807866

3 March 2009 Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

Ye Xu, Michael C. Lee, Lilla Boroczky, Aaron D. Cann, Alain C. Borczuk, Steven M. Kawut, Charles A. Powell

Author Affiliations +

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600Z (2009) https://doi.org/10.1117/12.807866
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Ye Xu, Michael C. Lee, Lilla Boroczky, Aaron D. Cann, Alain C. Borczuk, Steven M. Kawut, and Charles A. Powell "Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600Z (3 March 2009); https://doi.org/10.1117/12.807866

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