Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images.
Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images.
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DOI：
10.1371/journal.pone.0035550
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Kobayashi TJ
中科院分区：
文献类型：
--
作者：
Bashar MK;Komatsu K;Fujimori T;Kobayashi TJ
在许多生物学研究中，使用三维（3D）显微图像精确识别细胞核及其跟踪是一项要求很高的任务。从图像中手动识别细胞核形心是一项容易出错的任务，有时由于对比度低和噪音的存在而无法完成。尽管如此，只有少数方法可用于3D生物成像应用，这与已经存在许多方法的2D分析形成鲜明对比。此外，大多数方法基本上采用分割，其可靠的解决方案仍然是未知的，特别是对于具有并列细胞的3D生物图像。在这项工作中，我们提出了一种新的方法，可以直接从荧光显微镜图像中提取核质心。该方法包括三个步骤：（i）预处理，（ii）局部增强，和（iii）质心提取。第一步包括两个变体：第一变体（变体-1）使用整个3D预处理图像，而第二变体（变体-2）将预处理图像修改为候选区域或候选混合图像以进行进一步处理。在第二步中，采用多尺度立方体滤波以局部增强预处理后的图像。第三步中的质心提取包括三个阶段。在第一阶段，我们计算在每个体素的局部特征比，并提取局部极大值区域作为候选质心使用的比率阈值。阶段2处理通过分析来自增强图像的强度分布的形状来从阶段1结果中去除伪质心。然后提出了一种基于最近邻原理的迭代过程，如果存在碎片核，则进行联合收割机组合。对一组100张小鼠胚胎三维图像进行了定性和定量分析。研究揭示了在平均灵敏度和精确度方面所呈现的技术的有希望的成就（即，变异体-1和变异体-2分别为88.04%和91.30%和86.19%和95.00%）。elegans图片
Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i) Pre-processing, (ii) Local enhancement, and (iii) Centroid extraction. The first step includes two variations: first variation (Variant-1) uses the whole 3D pre-processed image, whereas the second one (Variant-2) modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2), when compared with an existing method (86.06% and 90.11%), originally developed for analyzing C. elegans images.
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影响因子：
10.6
作者：
Dzyubachyk, Oleh;van Cappellen, Wiggert A.;Meijering, Erik
通讯作者：
Meijering, Erik
影响因子：
2.7
作者：
Schnabel, R;Hutter, H;Schnabel, H
通讯作者：
Schnabel, H
影响因子：
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作者：
Lakshmanan, V.
通讯作者：
Lakshmanan, V.
生物图像信息学：工程生物学的新领域。
影响因子：
5.8
作者：
Peng, Hanchuan
通讯作者：
Peng, Hanchuan
影响因子：
3.3
作者：
Parfitt DE;Zernicka-Goetz M
通讯作者：
Zernicka-Goetz M