Koyuncu Can Fahrettin, Akhan Ece, Ersahin Tulin, Cetin-Atalay Rengul, Gunduz-Demir Cigdem
Computer Engineering Department, Bilkent University, Ankara, TR-06800, Turkey.
Molecular Biology and Genetics Department, Bilkent University, Ankara, TR-06800, Turkey.
Cytometry A. 2016 Apr;89(4):338-49. doi: 10.1002/cyto.a.22824. Epub 2016 Mar 4.
Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry.
自动化显微镜成像系统有助于分子细胞生物学研究中的高通量筛选。这些系统的第一步是细胞核分割，这对整个系统的成功有着重大影响。标记控制分水岭算法是先前研究中常用于细胞核分割的技术。这些研究通过在强度/梯度和/或距离变换图上寻找区域最小值来定义其标记。他们通常事先使用h最小值变换来抑制这些图上的噪声。h值的选择至关重要；过小的值不能充分抑制噪声，导致错误和过度分割的标记，而过大的值会抑制过多像素，导致标记缺失和分割不足。由于细胞核在图像中表现出不同的特征，相同的h值可能无法为所有细胞核定义正确的标记。为了解决这个问题，在这项工作中，我们提出了一种新的分水岭算法，该算法考虑一组不同的h值来迭代识别其标记。在每次迭代中，所提出的算法使用特定的h值定义一组候选标记，并从那些满足大小要求的候选标记中选择标记。通过宽场荧光显微镜图像进行的实验表明，与其他算法相比，我们的迭代算法中使用多个h值能带来更好的分割结果。© 2016国际细胞计量学促进协会。
