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.

JF - Applications of Discrete Geometry and Mathematical Morphology (WADGMM) T3 - Lecture Notes in Computer Science PB - Springer Verlag CY - Berlin; Heidelberg; New York; London; Paris; Tokyo N1 - ScopusID: 84865454250doi: 10.1007/978-3-642-32313-3_8 JO - LNCS ER - TY - CHAP T1 - Machine learning for supporting binary tomographic reconstruction T2 - Workshop on Applications of Discrete Geometry in Mathematical Morphology Y1 - 2010 A1 - Péter Balázs A1 - Mihály Gara A1 - Tamás Sámuel Tasi ED - Ullrich Köthe ED - Annick Montanvert ED - Pierre Soille JF - Workshop on Applications of Discrete Geometry in Mathematical Morphology T3 - Lecture Notes in Computer Science PB - Springer CY - Istambul, Turkey JO - LNCS ER -