Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF

Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF
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使用高效间隙搜索 MRF 对彩色宫颈涂片图像进行自动细胞质和细胞核分割

DOI:
10.1016/j.compbiomed.2016.01.025
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发表时间:
2016-04
影响因子:
7.7
通讯作者:
Zhu, Chengzhang
Zhu, Chengzhang
中科院分区:
工程技术2区
文献类型:
--
作者:
Zhu, En;Wu, Chengkun;Wang, Siqi;Zhu, Chengzhang

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自动化宫颈细胞分析系统需要准确有效的宫颈涂片图像分割。因此,我们提出了一种新的基于超像素的马尔可夫随机场(MRF)分割框架来获取细胞图像的细胞核,细胞质和图像背景。我们寻求分类的颜色非重叠的超像素补丁图像分割的一个图像。该模型将整个图像描述为无向概率图形模型,并使用自动标签映射机制来确定细胞核、细胞质和背景区域。设计了间隙搜索算法以提高模型的效率。数据表明,我们的框架的算法为现实世界和公共Herlev数据集提供了更好的准确性。此外,该模型的间隙搜索算法比基于像素和基于超像素的算法快得多。
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.
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发表时间: 2014-02
期刊: 2014 World Congress on Computing and Communication Technologies
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