title: Graph-based segmentation of abnormal nuclei in cervical cytology.
abstract: A general method is reported for improving the segmentation of abnormal cell nuclei in cervical cytology images. In automation-assisted reading of cervical cytology, one of the essential steps is the segmentation of nuclei. Despite some progress, there is a need to improve the sensitivity, particularly the segmentation of abnormal nuclei. Our method starts with pre-segmenting the nucleus to define the coarse center and size of nucleus, which is used to construct a graph by image unfolding that maps ellipse-like border in the Cartesian coordinate system to lines in the polar coordinate system. The cost function jointly reflects properties of nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are utilized to modify the local cost functions. The globally optimal path in the constructed graph is then identified by dynamic programming with an iterative approach ensuring an optimal closed contour. Validation of our method was performed on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained manual liquid-based cytology (HEMLBC). Compared with five state-of-the-art approaches, our graph-search based method shows superior performance.
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA. Electronic address: ling-zhang@uiowa.edu.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Kong
authors.affiliation: Department of Pathology, People's Hospital of Nanshan District, Shenzhen 518052, China.
authors.forename: Shaoxiong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China. Electronic address: tfwang@szu.edu.cn.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China. Electronic address: chensiping@szu.edu.cn.
authors.forename: Siping
authors.initials: S
authors.lastname: Chen
authors.affiliation: Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Milan
authors.initials: M
authors.lastname: Sonka
mesh_terms: D000465:Algorithms; D001331:Automation; D002467:Cell Nucleus / Q000473:pathology*; D002584:Cervix Uteri / Q000473:pathology*; D003584:Cytological Techniques; D003593:Cytoplasm; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015203:Reproducibility of Results; D013194:Staining and Labeling
keywords: Abnormal nuclei; Cervical cells; Graph-based segmentation; Image cytometry; Segmentation