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.
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