AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES.
AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES.
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DOI：
10.1109/isbi.2009.5193169
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Wang MD
中科院分区：
文献类型：
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作者：
Kothari S;Chaudry Q;Wang MD
本文提出了一种新的，快速和半自动的方法，准确的细胞簇分割和细胞计数的数字组织图像样本。在病理条件下，复杂的细胞簇是组织样品中的突出特征。这些簇的分割是开发准确细胞计数方法的主要挑战。我们通过以下三个步骤来解决聚类分割的问题。第一步涉及从RGB组织样本获得适当的细胞核簇边界图像所需的预处理。第二步涉及在簇的边缘处的重叠检测，以找到两个核之间的重叠点。第三步涉及分割在这些凹处通过使用椭圆拟合技术。一旦聚类被分割，单个细胞核被计数以给出细胞计数。该方法在四种不同类型的癌组织样本上进行了测试，并显示出具有低百分比误差、高真阳性率和低假发现率的有希望的结果。
This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.