Learning orientation invariant contextual features for nodule detection in lung CT scans

This work combines model-based local shape analysis and data-driven local contextual feature learning for improved detection of pulmonary nodules in low dose computed tomography (LDCT) chest scans. We reduce orientation-induced appearance variability by performing intensity-weighted principal component analysis (PCA) to estimate the local orientation at each candidate location. Random comparison primitives defined in a local coordinate system are used to describe the local context around a nodule candidate. A random forest is trained to learn and combine a subset of these primitives into discriminative orientation invariant contextual features and classify nodule candidates. Validation using 99 CT scans from the publicly available Lung Image Database Consortium (LIDC) demonstrates the benefit of combining geometric modeling and data-driven machine learning. The proposed method reduces more than 80% of false positives of the baseline model-based method consistently over a wide range of sensitivity levels (70%-90%).