Statistical pattern recognition algorithms for autofluorescence imaging
2009
In cancer diagnostics the most important problems are the early identification and estimation of the tumor growth and
spread in order to determine the area to be operated. The aim of the work was to design of statistical algorithms helping
doctors to objectively estimate pathologically changed areas and to assess the disease advancement. In the research,
algorithms for classifying endoscopic autofluorescence images of larynx and intestine were used. The results show that
the statistical pattern recognition offers new possibilities for endoscopic diagnostics and can be of a tremendous help in
assessing the area of the pathological changes.
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