Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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