title: Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF.
abstract: Accurate and effective cervical smear image segmentation is required for automated cervical cell analysis systems. Thus, we proposed a novel superpixel-based Markov random field (MRF) segmentation framework to acquire the nucleus, cytoplasm and image background of cell images. We seek to classify color non-overlapping superpixel-patches on one image for image segmentation. This model describes the whole image as an undirected probabilistic graphical model and was developed using an automatic label-map mechanism for determining nuclear, cytoplasmic and background regions. A gap-search algorithm was designed to enhance the model efficiency. Data show that the algorithms of our framework provide better accuracy for both real-world and the public Herlev datasets. Furthermore, the proposed gap-search algorithm of this model is much more faster than pixel-based and superpixel-based algorithms.
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China. Electronic address: yilinzhao@nudt.edu.cn.
authors.forename: Lili
authors.initials: L
authors.lastname: Zhao
authors.affiliation: Institute of Software, College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Kuan
authors.initials: K
authors.lastname: Li
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Mao
authors.initials: M
authors.lastname: Wang
authors.affiliation: State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China.
authors.forename: Jianping
authors.initials: J
authors.lastname: Yin
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: En
authors.initials: E
authors.lastname: Zhu
authors.affiliation: Institute of Software, College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Chengkun
authors.initials: C
authors.lastname: Wu
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Siqi
authors.initials: S
authors.lastname: Wang
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Chengzhang
authors.initials: C
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014626:Vaginal Smears
keywords: Cervical smear image segmentation; Faster MRF; MRF modeling and inference; Papanicolaou test; Superpixel feature extraction and selection; Superpixel-based MRF