TY - GEN
T1 - Data Mining Techniques for AFM- Based Tumor Classification
AU - Hutterer, Stephan
AU - Zauner, Gerald
AU - Schilcher, Kurt
AU - Sylie, Rene
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Atomic Force Microscopy
KW - Genetic Programming
KW - Tumor Classification
UR - http://www.scopus.com/inward/record.url?scp=84864064178&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2012.6217218
DO - 10.1109/CIBCB.2012.6217218
M3 - Conference contribution
SN - 9781467311892
T3 - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
SP - 105
EP - 111
BT - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
PB - IEEE Computational Intelligence Society
T2 - IEEE Computational Intelligence in Bioinformatics and Computational Biology - CIBCB
Y2 - 9 May 2012 through 12 May 2012
ER -