A Review of Automated Techniques for Cervical Cell Image Analysis and Classification

Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 4)


Cervical smear screening is the most popular method used for the detection of cervical cancer in its early stages. The most eminent screening test is the Pap smear, which is based on the staining of cervical cells, using the technique that was first introduced by George Papanicolaou (Science 1942). With this screening technique, precancerous conditions and abnormal changes in cells that may develop into cancer are recognized. The widespread use of this test in developed countries has significantly reduced the incidence and mortality of invasive cervical cancer. In the last years, many methods have been appeared in the literature, which aim at the automated determination of the cytoplasm and the nucleus in these images. In this context, sophisticated image processing techniques and feature extraction and classification methods have been developed by several researchers, in order to derive useful conclusions for the characterization of the contents of the Pap smear images. In this work, an overview of the published techniques related to cervical smear screening is presented, in order to provide an integrated essay of the state of the art methods in the specific scientific field. Special focus has been paid on two main concepts with great research interest: the cell image segmentation and the classification techniques proposed for the characterization Pap smear images.


Pap smear images Cell nuclei segmentation Abnormal cell feature extraction 


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of IoanninaIoanninaGreece

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