Abstract
To get the best possible chance of healing, cancer has to be detected as early as possible. As cancer starts within a single cell, cytopathological methods offer the possibility of early detection. One such method is standardized DNA image cytometry. For this, the diagnostically relevant cells have to be found within each specimen, which is currently performed manually. Since this is a time-consuming process, a preselection of diagnostically relevant cells has to be performed automatically. For specimens of the oral mucosa this involves distinguishing between undoubted healthy epithelial cells and possibly cancerous epithelial cells. Based on cell images from a brightfield light microscope, a set of morphological and textural features was implemented. To identify highly distinctive feature subsets the sequential forward floating search method is used. For these feature sets k-nearest neighbor and fuzzy k-nearest neighbor classifiers as well as support vector machines were trained. On a validation set of cells it could be shown that normal and possibly cancerous cells can be distinguished at overall rates above 95.5 % for different classifiers, enabling us to choose the support vector machine with a set of two features only as the classifier with the lowest computational costs.
Highlights
Cancer is the second leading cause of death in industrial countries
To solve the classification task, different classification algorithms can be chosen. Each of these has to be trained on a training set of cells, whereas the classification rate is calculated on an independent cell set
It can be seen that chromatin features provide a good separability performance, whereas only the geometrical features within the basic set morphology do not distinguish the two classes sufficiently
Summary
As to have the best possible chance of cure, cancer has to be detected and treated as early as possible. As cancer starts within a single cell, many types of cancer can be detected very early from already marginal changes within single cells using cytopathological methods. These cell specimens can be obtained and painlessly, e.g. with tiny brushes, from the oral mucosa. One cytopathological diagnostic method is DNA image cytometry (DNA-ICM). For this the cells are stained stoichiometrically according to Feulgen to visualize the DNA content within the nuclei. Images of the nuclei are captured with a camera mounted on a brightfield light microscope
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