Discrete tomography by convex-concave regularization and D.C. programming

  • Authors:

  • T. Schüle;C. Schnörr;S. Weber;J. Hornegger

  • Affiliations:

  • Department of M&CS, University of Mannheim, CVGPR-Group, D-68131 Mannheim, Germany and Siemens Medical Solutions, D-91301 Forchheim, Germany;Department of M&CS, University of Mannheim, CVGPR-Group, D-68131 Mannheim, Germany;Department of M&CS, University of Mannheim, CVGPR-Group, D-68131 Mannheim, Germany;Department of M&CS, Friedrich-Alexander University Erlangen-Nüürnberg, D-91058 Erlangen, Germany

  • Venue:
  • Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
  • Year:
  • 2005

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Abstract

We present a novel approach to the tomographic reconstruction of binary objects from few projection directions within a limited range of angles. A quadratic objective functional over binary variables comprising the squared projection error and a prior penalizing non-homogeneous regions, is supplemented with a concave functional enforcing binary solutions. Application of a primal-dual subgradient algorithm to a suitable decomposition of the objective functional into the difference of two convex functions leads to an algorithm which provably converges with parallel updates to binary solutions. Numerical results demonstrate robustness against local minima and excellent reconstruction performance using five projections within a range of 90^@?. Our approach is applicable to quite general objective functions over binary variables with constraints and thus applicable to a wide range of problems within and beyond the field of discrete tomography.