School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
J Microsc. 2013 Apr;250(1):68-75. doi: 10.1111/jmi.12019. Epub 2013 Feb 15.
Zebrafish is an invaluable vertebrate model in life science research and has been widely used in biological pathway analysis, molecular screening and disease modelling, among others. As a result, microscopic imaging has become an essential step in zebrafish phenotype analysis, and image segmentation thus plays an important role in the zebrafish microscopy analysis. Due to the nonuniform distribution of intensity and weak boundary in zebrafish microscope images, the traditionally used segmentation methods may lead to unsatisfactory result. Here, a novel hybrid method that integrates region and boundary information into active contour model is proposed to segment zebrafish embryos from the background, which performs better than traditional segmentation models. Meanwhile, how to utilize the gradient information effectively in image segmentation is still an open problem. In this paper, we propose to improve the aforementioned hybrid method in two aspects. Firstly, the mean grey value of background is estimated by the expectation maximization (EM) algorithm to constrain the active curve evolution. Secondly, an edge stopping function sensitive to gradient information is designed to stop curve evolution when the active curve reaches the embryo boundary. Experimental results show that the proposed methods can provide superior segmentation results compared to existing algorithms.
斑马鱼是生命科学研究中一种非常有价值的脊椎动物模型，已广泛应用于生物途径分析、分子筛选和疾病建模等领域。因此，微观成像已成为斑马鱼表型分析的一个必要步骤，而图像分割在斑马鱼显微镜分析中起着重要作用。由于斑马鱼显微镜图像的强度分布不均匀和边界较弱，传统的分割方法可能导致不理想的结果。在这里，提出了一种将区域和边界信息集成到主动轮廓模型中的新型混合方法，用于从背景中分割斑马鱼胚胎，该方法比传统的分割模型表现更好。同时，如何在图像分割中有效地利用梯度信息仍然是一个悬而未决的问题。在本文中，我们提出了两个方面来改进上述混合方法。首先，通过期望最大化（EM）算法估计背景的平均灰度值，以约束主动曲线的演化。其次，设计了一个对梯度信息敏感的边缘停止函数，当主动曲线到达胚胎边界时，停止曲线的演化。实验结果表明，与现有算法相比，所提出的方法可以提供更好的分割结果。
