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BioMed Research International
Volume 2013 (2013), Article ID 176519, 11 pages
Research Article

Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

1School of Applied Health and Social Sciences, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria
2Department of Pathology, Nerve Clinic Linz Wagner Jauregg, Wagner-Jauregg-Weg 15, 4020 Linz, Austria
3University of Applied Sciences Upper Austria, Research & Development Wels, Stelzhamerstraße 23, 4600 Wels, Austria

Received 30 April 2013; Accepted 18 July 2013

Academic Editor: Kaisorn L. Chaichana

Copyright © 2013 Marlene Huml et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.