On the importance of nucleus features in the classification of cervical cells in Pap smear images

In this work, we investigate the classification of cer-vical cells by exploiting only the nucleus features and not taking into account the features extracted from the cytoplasm area. This procedure is motivated by the fact that the nuclei areas can be extracted automati-cally from Pap smear images, in contrast to the cyto-plasm segmentation which is not a solved problem yet. Furthermore, we consider the representation of these features in low dimensional spaces using non linear di-mensionality reduction techniques, which can properly handle complex nonlinear data, as they better describe their manifold structure. The classification was per-formed in a supervised manner, using support vector machines (SVM) with several kernel functions. The ob-tained results indicate that we can achieve high clas-sification performance when we use features extracted only from the nuclei areas. 1