Multi-level classification of emphysema in HRCT lung images using delegated classifiers.

Emphysema is a common chronic respiratory disorder characterized by the destruction of lung tissue. It is a progressive disease where the early stages are characterized by diffuse appearance of small air spaces and later stages exhibit large air spaces called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, we show that an automated texture-based system based on delegated classifiers is capable of achieving multiple levels of emphysema extraction in High Resolution Computed Tomography (HRCT) images. The key idea of delegation is that a cautious classifier makes predictions that meet a minimum level of confidence, and delegates the difficult or uncertain predictions to a more specialized classifier. In this paper, we design a two-step scenario where a first classifier chooses the examples to classify on and delegates the more difficult examples to a second classifier. We compare this technique to well known emphysema classification techniques and ensemble methods such as bagging and boosting. Comparison of the results shows that the techniques presented here are more accurate. From a medical standpoint, the classifiers built at different iterations appear to show an interesting correlation with different levels of emphysema.