Data Mining Techniques for AFM- Based Tumor Classification

Stephan Hutterer, Gerald Zauner, Kurt Schilcher, Rene Sylie

Research output: Chapter in Book/Report/Conference proceedingsConference contribution

3 Citations (Scopus)

Abstract

The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterization of histological brain tumor samples. Data mining techniques will be applied for automatic identification of brain tumor tissues based on AFM images by means of classifying grade II and IV tumors. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler- Poincaré characteristic) as a feature descriptor to characterize global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.

Original languageEnglish
Title of host publication2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
PublisherIEEE Computational Intelligence Society
Pages105-111
Number of pages7
ISBN (Print)9781467311892
DOIs
Publication statusPublished - 2012
EventIEEE Computational Intelligence in Bioinformatics and Computational Biology - CIBCB - San Diego, CA, USA, United States
Duration: 9 May 201212 May 2012

Publication series

Name2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

Conference

ConferenceIEEE Computational Intelligence in Bioinformatics and Computational Biology - CIBCB
CountryUnited States
CitySan Diego, CA, USA
Period09.05.201212.05.2012

Keywords

  • Atomic Force Microscopy
  • Genetic Programming
  • Tumor Classification

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