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Sparse Representation Using Block Decomposition for Characterization of Imaging Patterns

  • Keni Zheng
  • Sokratis MakrogiannisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Mathematical Sciences DepartmentDelaware State UniversityDoverUSA

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