斑马鱼是一种当今应用十分广泛的模式生物,研究其实验图像的信息自动提取具有较高的应用价值。药物培养斑马鱼胚胎在发育成长过程中的生理影响的定量分析,很难通过人工肉眼观察的方式得出较为准确的指标值,通常要借助于计算机。斑马鱼图像定量分析的前提基础就是可以对斑马鱼胚胎图像,按照斑马鱼的生理结构即头部、躯干和卵黄三个部分在图像中分割开。但是实际情况是斑马鱼药物实验中特定药物的实验组数量有限,不能通过学习训练等机器学习的方式分割,只能使用图像处理建模分割。本文使用距离变换结合分水岭算法、减法聚类结合K-means聚类算法以及减法聚类结合漫水填充算法分别对图像进行语义分割,发现减法聚类结合漫水填充算法的分割效果达到了满足研究所需的生理结构分割的目的,为后续医学研究中的定量分析奠定了较好的基础。

Zebrafish, a model organism, is widely used today. Quantitative analysis of the physiological effects of drug-cultured zebrafish embryos during development and obtaining accurate index values by visual observation is difficult. Therefore, it is necessary to use computers. The basis for the quantitative analysis of zebrafish images is that the zebrafish embryo images can be segmented in the image according to the zebrafish’s physiological structure, i.e. the head, trunk and yolk. However, because of the limited number of experimental groups for specific drugs in zebrafish drug experiments, they cannot be segmented by machine learning such as deep learning, and only image processing modeling can be used for segmentation. In this paper, the image was semantically segmented by using distance transformation combined with watershed algorithm, subtraction clustering combined with K-means clustering algorithm and subtraction clustering combined with flood-filling algorithm. Finally, the physiological structure segmentation effect of flood-filling algorithm combined with subtraction clustering meet the purpose of the study, and the segmentation laid a good foundation for subsequent quantitative analysis in the medical research.