Sparse sampling and unsupervised learning of lung texture patterns in pulmonary emphysema: MESA COPD study

Pulmonary emphysema is defined morphologically by enlargement of alveolar airspaces and manifests as textural differences on thoracic computed tomography (CT). This work presents an unsupervised approach to extract the most dominant local lung texture patterns on CT scans. Since the method does not use manually annotated labels restricted to predefined emphysema subtypes, it can be used for discovery of novel image-based phenotypes with greater efficiency and reliability. This study demonstrates the applicability of the learned patterns for content-based image retrieval.