In this paper we investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or - with the aid of the knowledge acquired - to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers. © 2012 Springer-Verlag.

%B Applications of Discrete Geometry and Mathematical Morphology (WADGMM) %S Lecture Notes in Computer Science %I Springer Verlag %C Berlin; Heidelberg; New York; London; Paris; Tokyo %P 109 - 124 %8 Aug 2012 %G eng %9 Conference paper %! LNCS %R 10.1007/978-3-642-32313-3_8 %0 Book Section %B Workshop on Applications of Discrete Geometry in Mathematical Morphology %D 2010 %T Machine learning for supporting binary tomographic reconstruction %A Péter Balázs %A Mihály Gara %A Tamás Sámuel Tasi %E Ullrich Köthe %E Annick Montanvert %E Pierre Soille %B Workshop on Applications of Discrete Geometry in Mathematical Morphology %S Lecture Notes in Computer Science %I Springer %C Istambul, Turkey %P 101 - 105 %8 Aug 2010 %G eng %9 Conference paper %! LNCS