Object classification in images for Epo doping control based on fuzzy decision trees

Ivan Bajla, Igor Holländer, Dorothea Heiss, Reinhard Granec, Markus Minichmayr

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung


Erythrpoietin (Epo) is a hormone which can be misused as a doping substance. Its detection involves analysis of images containing specific objects (bands), whose position and intensity are critical for doping positivity. Within a research project of the World Anti-Doping Agency (WADA) we are implementing the GASepo software that should serve for Epo testing in doping control laboratories world-wide. For identification of the bands we have developed a segmentation procedure based on a sequence of filters and edge detectors. Wheras all true bands are properly segmented, the procedure generates a relatively high number of false positivies (artefacts). To seperate these artefacts we suggested a post-segmentation suprvised classification using-valued geometrical measures of objects. The method is basedon the ID3 (Ross Quinlan's) rule generation method, where fuzzy representation is used for linking the linguistic terms to quantitative data. The fuzzy modification of the ID3 method provides a framework that generates fuzzy decision trees, as well as fuzzy sets for input data. Using the MLFTM software (Machine Learning Framework) we have generated a set of fuzzy rules explicity describing bands and artefacts. The method eliminated most of the artefacts. The contribution includes a comparison of the obtained misclassificatino errors to the errors produced by some other statistical classification methods.
TitelElectronic Imaging Applications of Neural Networks and Machine Learning in Image Processing IX
PublikationsstatusVeröffentlicht - 2005
VeranstaltungElectronic Imaging -
Dauer: 1 Jan. 2005 → …


KonferenzElectronic Imaging
Zeitraum1/01/05 → …

Research Field

  • Nicht definiert


  • Epo doping control
  • image segmentation
  • machine learning classification
  • fuzzy decision tree


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