Cervical Cell Classification Based Exclusively on Nucleus Features

Marina E. Plissiti, Christophoros Nikou

Abstract: In this work, we present a framework for the efficient classification of cervical cells in normal and abnormal categories, based on features extracted exclusively from the nucleus area and ignoring the contingent cytoplasm features. This task is very important, since the nuclei are the only distinguishable areas in complex Pap smear images, as these images present a high degree of cell overlapping and the exact borders of the cytoplasm areas are ambiguous. We have examined the ability of non-linear dimensionality reduction schemes to produce accurate representation of the features manifold, along with the definition of an efficient feature subset, and their influence on the classification performance. Two unsupervised classifiers were used and the results indicate that we can achieve high classification performance when only the nuclei features are used.