01367nas a2200181   4500008004100000245006900041210006900110260008200179300001400261520067500275100001800950700002500968700002000993700002001013700002301033700001901056856011001075       2012                        eng d00aMachine learning as a preprocessing phase in discrete tomography0 aMachine learning as a preprocessing phase in discrete tomography  aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer VerlagcAug 2012  a109 - 1243 a<p>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.</p>1 aGara, Mihály1 aTasi, Tamás Sámuel1 aBalázs, Péter1 aKöthe, Ullrich1 aMontanvert, Annick1 aSoille, Pierre  uhttps://home.inf.u-szeged.hu/publication/machine-learning-as-a-preprocessing-phase-in-discrete-tomography