Nuclei Detection Based on Secant Normal Voting with Skipping Ranges in Stained Histopathological Images
Nuclei Detection Based on Secant Normal Voting with Skipping Ranges in Stained Histopathological Images
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DOI：
10.1587/transinf.2017edp7326
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发表时间：
2018-02
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影响因子：
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通讯作者：
XueTing Lim;Kenjiro Sugimoto;S. Kamata
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文献类型：
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作者：
XueTing Lim;Kenjiro Sugimoto;S. Kamata
种子检测或有时称为细胞核检测是细胞核分割的先决条件，在定量细胞分析中起着关键作用。如果每个检测到的种子仅位于一个核中并且靠近核中心，则检测结果被认为是准确的。在以前的工作中，投票方法被用来检测核中心提取核的显著性特征。然而，这些方法仍然遇到错误播种的风险，特别是对于非均匀强度图像。为了克服以往工作的不足，提出了一种新的检测方法，这是所谓的正割正常投票。在所提出的跳跃范围内，Se-cant正常投票取得了良好的性能。跳过范围通过防止遮挡区域上的错误播种来避免过度分割。核中心是通过均值漂移聚类从云的投票点。在实验中，我们表明，我们提出的方法优于比较方法，通过实现高检测精度，而不牺牲计算效率。
SUMMARY Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Se-cant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational e ffi ciency.