Generation of Digital Phantoms of Cell Nuclei and Simulation of Image Formation in 3D Image Cytometry
Generation of Digital Phantoms of Cell Nuclei and Simulation of Image Formation in 3D Image Cytometry
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DOI：
10.1002/cyto.a.20714
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发表时间：
2009-06-01
期刊：
影响因子：
3.7
通讯作者：
Stejskal, Stanislav
中科院分区：
文献类型：
--
作者：
Svoboda, David;Kozubek, Michal;Stejskal, Stanislav
图像细胞术仍然面临着细胞图像分析结果质量的问题。细胞制备、光学和电子学引起的退化对使用光学显微镜获取的大多数2D和3D细胞图像数据有很大影响。这就是为什么应用于这些数据的图像处理算法通常提供不精确和不可靠的结果。由于在大多数实验中无法获得给定图像数据的基本真实情况，不同图像分析方法的输出既不能被验证，也不能相互比较。一些论文通过该领域专家(生物学家或内科医生)对基本事实的估计，部分解决了这个问题。然而，在许多情况下，这样的基本事实估计是非常主观的，不同的专家之间存在很大的差异。为了克服这些困难，我们创建了一个工具箱，可以生成特定细胞组件的3D数字模型以及它们被特定光学和电子学退化的相应图像。然后，用户可以将图像分析方法应用于这样的模拟图像数据。可以将分析结果(例如分割或测量结果)与从输入对象数字模型(或其上的测量)导出的地面真实进行比较。这样，图像分析方法可以相互比较，并且可以计算它们的质量(基于与地面真实情况的差异)。我们还评估了合成图像的可信性，通过它们与真实图像数据的相似性来衡量。我们测试了几个相似性标准，如视觉比较、强度直方图、中心矩、频率分析、熵和3D Haralick特征。结果表明，真实图像数据与模拟图像数据具有较高的相似性。(C)2009年国际细胞测量促进会
Image cytometry still faces the problem of the quality of cell image analysis results. Degradations caused by cell preparation, optics, and electronics considerably affect most 2D and 3D cell image data acquired using optical microscopy. That is why image processing algorithms applied to these data typically offer imprecise and unreliable results. As the ground truth for given image data is not available in most experiments, the outputs of different image analysis methods can be neither verified nor compared to each other. Some papers solve this problem partially with estimates of ground truth by experts in the field (biologists or physicians). However, in many cases, such a ground truth estimate is very subjective and strongly varies between different experts. To overcome these difficulties, we have created a toolbox that can generate 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The user can then apply image analysis methods to such simulated image data. The analysis results (such as segmentation or measurement results) can be compared with ground truth derived from input object digital phantoms (or measurements on them). In this way, image analysis methods can be compared with each other and their quality (based on the difference from ground truth) can be computed. We have also evaluated the plausibility of the synthetic images, measured by their similarity to real image data. We have tested several similarity criteria such as visual comparison, intensity histograms, central moments, frequency analysis, entropy, and 3D Haralick features. The results indicate a high degree of similarity between real and simulated image data. (C) 2009 International Society for Advancement of Cytometry