  Pulmonary emphysema is traditionally subcategorized into three subtypes,
which have distinct radiological appearances on computed tomography (CT) and
can help with the diagnosis of chronic obstructive pulmonary disease (COPD).
Automated texture-based quantification of emphysema subtypes has been
successfully implemented via supervised learning of these three emphysema
subtypes. In this work, we demonstrate that unsupervised learning on a large
heterogeneous database of CT scans can generate texture prototypes that are
visually homogeneous and distinct, reproducible across subjects, and capable of
predicting accurately the three standard radiological subtypes. These texture
prototypes enable automated labeling of lung volumes, and open the way to new
interpretations of lung CT scans with finer subtyping of emphysema.
