Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Huml:2013:BMRI,
  author =       "Marlene Huml and Rene Silye and Gerald Zauner and 
                 Stephan Hutterer and Kurt Schilcher",
  title =        "Brain Tumor Classification Using {AFM} in Combination
                 with Data Mining Techniques",
  journal =      "BioMed Research International",
  year =         "2013",
  month =        aug # "~25",
  pages =        "Article ID 176519",
  keywords =     "genetic algorithms, genetic programming, GP",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  language =     "en",
  publisher =    "Hindawi Publishing Corporation",
  oai =          "oai:pubmedcentral.nih.gov:3766995",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766995",
  URL =          "http://dx.doi.org/10.1155/2013/176519",
  size =         "11 pages",
  abstract =     "Although classification of astrocytic tumours is
                 standardised by the WHO grading system, which is mainly
                 based on microscopy-derived, histomorphological
                 features, there is great inter-observer variability.
                 The main causes are thought to be the complexity of
                 morphological details varying from tumour to tumour 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
                 94.74percent classification accuracy in distinguishing
                 grade II tumours from grade IV ones. While using modern
                 image analysis techniques, AFM may become an important
                 tool in astrocytic tumour diagnosis. By this way
                 patients suffering from grade II tumours are identified
                 unambiguously, having a less risk for malignant
                 transformation. They would benefit from early adjuvant
                 therapies.",
}

Genetic Programming entries for Marlene Huml Rene Silye Gerald Zauner Stephan Hutterer Kurt Schilcher

Citations