Classification of image objects in Epo doping control using fuzzy decision tree

Erythropoietin (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 serves for Epo testing in doping control laboratories worldwide. For identification of the bands we have developed a segmentation procedure based on a sequence of filters. Whereas all true bands are properly segmented, the procedure generates a number of false positives (artefacts). To separate these artefacts we suggested a post-segmentation supervised classification using real-valued geometrical measures of objects. The method is based on a fuzzy modification of Ross Quinlan's ID3 method, included in the mlf™ software (Machine Learning Framework). It provides a framework that generates fuzzy decision trees, as well as fuzzy sets for input data. Initially used training set of segmented objects has been replaced by a new one prepared by more accurate expertise using the latest release of the GASepo software. The new fuzzy decision trees (FDT) have been generated for a set of five and nine fuzzy sets. The comparison of the results on testing set of segmented objects shows that the classification based on the new FDTs outperforms other classification methods.