Sparse Fisher Discriminant Analysis for Computer Aided Detection

We describe a method for sparse feature selection for a class of problems motivated by our work in Computer-Aided Detection (CAD) systems for identifying structures of interest in medical images. Typical CAD data sets for classification are large (several thousand candidates) and unbalanced (significantly fewer than 1% of the candidates are ”positive”). To be accepted by physicians, CAD systems must generalize well with extremely high sensitivity and very few false positives. In order to find the features that can lead to superior generalization, researchers typically generate a large number of experimental features for each candidate. The reason for such a large number of features is that there are no definitive methods for capturing the shape and image-based characteristics that correspond to the diagnostic features used by physicians to identify structures of interest in the image for example, cancerous polyps in a CT (computed tomography) volume of a patient’s colon. Thus several (100+) shape, texture, and intensity based features may be generated for each candidate at various levels of resolution. We propose a sparse formulation for Fisher Linear Discriminant (FLD) that scales well to large datasets; our method inherits all the desirable properties of FLD, while improving on handling large numbers of irrelevant and redundant features. We demonstrate that our sparse FLD formulation outperforms conventional FLD and two other methods for feature selection from the literature on both an artificial dataset and a real-world Colon CAD