Journal article
Graph-based segmentation of abnormal nuclei in cervical cytology
Computerized medical imaging and graphics, Vol.56, pp.38-48
03/2017
DOI: 10.1016/j.compmedimag.2017.01.002
PMCID: PMC5777156
PMID: 28222324
Abstract
•A general method for improving the segmentation of abnormal cell nuclei.•Nuclear shape constraint is embedded in the construction of the segmentation graph.•Nucleus border, region, and context are incorporated in a global optimal solution.•Our approach demonstrates superior performance on two cervical cell datasets.
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.
Details
- Title: Subtitle
- Graph-based segmentation of abnormal nuclei in cervical cytology
- Creators
- Ling Zhang - Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, ChinaHui Kong - School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaShaoxiong Liu - Department of Pathology, People's Hospital of Nanshan District, Shenzhen 518052, ChinaTianfu Wang - Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, ChinaSiping Chen - Department of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, ChinaMilan Sonka - Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- Computerized medical imaging and graphics, Vol.56, pp.38-48
- DOI
- 10.1016/j.compmedimag.2017.01.002
- PMID
- 28222324
- PMCID
- PMC5777156
- NLM abbreviation
- Comput Med Imaging Graph
- ISSN
- 0895-6111
- eISSN
- 1879-0771
- Publisher
- Elsevier Ltd
- Grant note
- DOI: 10.13039/100000002, name: NIH, award: R01EB004640; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 61427806, 81501545; DOI: 10.13039/501100002858, name: China Postdoctoral Science Foundation, award: 2014M552230
- Language
- English
- Date published
- 03/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047648302771
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