Introduction Automation-assisted reading techniques have the potential to reduce errors and increase productivity in cervical cancer screening. The key function of these tech-niques is to automatically distinguish potentially abnormal cells from a large numbers of normal cells in cervical cytology slides for further manual reading by pathologists. Both the nuclear and cytoplasmic morphological features are useful in distinguishing between abnormal and normal cells. However, recent studies have demonstrated the important role of nuclei in cancer recognition . More specifically, in cervical cytology diagnosis, all the cell abnormali-ties including atypical squamous cells of undetermined significance(ASC-US), ASC-cannot exclude HSIL (ASC-H), low grade squamous intraepithelial lesion (LSIL), high SIL (HSIL), and squamous cell carcinoma, accompany nuclear abnormality . In order to accurately characterize nuclear abnormality,reliable automated detection/segmentation of abnormal nuclei in cervical cytology is a necessary step, and is of utmost importance in automation-assisted reading techniques.