Automated Feature Selection for the Classification of Meningioma Cell Nuclei

Oliver Wirjadi, Thomas Breuel, Wolfgang Feiden, Yoo-Jin Kim
Bildverarbeitung für die Medizin, Informatik aktuell, Pages 76-80, Springer, 2006

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.

Files:

  OwTmbAutomFeatSelMenCellNuclei.pdf

BibTex:

@inproceedings{ WIRJ2006,
	Title = {Automated Feature Selection for the Classification of Meningioma Cell Nuclei},
	Author = {Oliver Wirjadi and Thomas Breuel and Wolfgang Feiden and Yoo-Jin Kim},
	BookTitle = {Bildverarbeitung für die Medizin},
	Year = {2006},
	Series = {Informatik aktuell},
	Publisher = {Springer},
	Pages = {76-80}
}

     
Last modified:: 30.08.2016