Abstract
A supervised learning method for image classification is presented which is independent of the type of images that will be processed. This is realized by constructing a large base of grey-value and colour based image features. We then rely on a decision tree to choose the features that are most relevant for a given application. We apply and evaluate our system on the classification task of meningioma cells.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wirjadi, O., Breuel, T.M., Feiden, W., Kim, YJ. (2006). Automated Feature Selection for the Classification of Meningioma Cell Nuclei. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2006. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32137-3_16
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DOI: https://doi.org/10.1007/3-540-32137-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32136-1
Online ISBN: 978-3-540-32137-8
eBook Packages: Computer Science and Engineering (German Language)