Proceedings Article | 26 March 2007
KEYWORDS: Lung, Shape analysis, Emphysema, Image classification, Signal attenuation, Computed tomography, Computer programming, Radiology, Visualization, Image segmentation
In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung
diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT
images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various
sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural
features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To
evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were
conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian
classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the
system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields
significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the
order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely
due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which
are most hard to differentiate for radiologists. According to these experimental results, adding shape features to
conventional texture features is much useful to improve classification performance of obstructive lung diseases in both
Bayesian and SVM classifiers.
