Sparse Representation Using Block Decomposition for Characterization of Imaging Patterns

Abstract

In this work we introduce sparse representation techniques for classification of high-dimensional imaging patterns into healthy and diseased states. We also propose a spatial block decomposition methodology that is used for training an ensemble of classifiers to address irregularities of the approximation problem. We first apply this framework to classification of bone radiography images for osteoporosis diagnosis. The second application domain is separation of breast lesions into benign and malignant. These are challenging classification problems because the imaging patterns are typically characterized by high Bayes error rate in the original space. To evaluate the classification performance we use cross-validation techniques. We also compare our sparse-based classification with state-of-the-art texture-based classification techniques. Our results indicate that decomposition into patches addresses difficulties caused by ill-posedness and improves original sparse classification.

S. Makrogiannis—This research was supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under Award Number SC3GM113754 and by Intramural Research Program of NIA, NIH (Contract Number HHSN271201600204P). We also acknowledge the support by CREOSA of Delaware State University funded by NSF CREST-8763.