Sparse Representation Using Block Decomposition for Characterization of Imaging Patterns
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.
- 9.Robere, R.: Interior point methods and linear programming. Technical report, University of Toronto (2012)Google Scholar
- 13.Zhao, W., Xu, R., Hirano, Y., Tachibana, R., Kido, S.: A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Comput. Math. Methods Med. 2015, 567932 (2015)Google Scholar
- 14.Zheng, K., Makrogiannis, S.: Bone texture characterization for osteoporosis diagnosis using digital radiography. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1034–1037, August 2016Google Scholar