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Computer Assisted Classification of Brain Tumors

  • Norbert Röhrl
  • José R. Iglesias-Rozas
  • Galia Weidl
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process.

We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.

Keywords

Support Vector Machine Brain Tumor Feature Space Histological Grade Learning Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Norbert Röhrl
    • 1
  • José R. Iglesias-Rozas
    • 2
  • Galia Weidl
    • 1
  1. 1.Institut für Analysis, Dynamik und ModellierungUniversitÄt StuttgartStuttgartGermany
  2. 2.Institut für Pathologie, NeuropathologieKatharinenhospitalStuttgartGermany

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