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Improved segmentation of abnormal cervical nuclei using a graph-search based approach

机译:使用基于图形搜索的方法改善了异常宫颈核的分割

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Reliable segmentation of abnormal nuclei in cervical cytology is of paramount importance in automation-assisted screening techniques. This paper presents a general method for improving the segmentation of abnormal nuclei using a graph-search based approach. More specifically, the proposed method focuses on the improvement of coarse (initial) segmentation. The improvement relies on a transform that maps round-like border in the Cartesian coordinate system into lines in the polar coordinate system. The costs consisting of nucleus-specific edge and region information are assigned to the nodes. The globally optimal path in the constructed graph is then identified by dynamic programming. We have tested the proposed method on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained liquid-based cytology (HELBC), and the comparative experiments with recent state-of-the-art approaches demonstrate the superior performance of the proposed method.
机译:宫颈细胞学中异常细胞核的可靠分割对于自动化辅助筛选技术至关重要。本文介绍了一种用于使用基于图形搜索的方法改善异常核的分割的一般方法。更具体地,所提出的方法侧重于改善粗(初始)分割。改进依赖于将笛卡尔坐标系中的圆形边框映射到极性坐标系中的圆形边框。由核特定边缘和区域信息组成的成本分配给节点。然后通过动态编程识别构造图中的全局最佳路径。从两种宫颈细胞图像数据集,Herlev和H&E染色的液体基细胞学(HELBC)测试了对异常核的方法,以及最近最先进的方法的比较实验表明了所提出的方法的优越性。

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