Chinese Academy of Sciences, Suzhou Institute of Biomedical Engineering and Technology, CAS Center f, China.
University of Sciences and Technology of China, College of Life Sciences, Baohe District, Hefei, China.
J Biomed Opt. 2018 Nov;23(11):1-7. doi: 10.1117/1.JBO.23.11.116503.
Phenotype analysis of yeast cell requires high-throughput imaging and automatic analysis of abundant image data. At first, each cell needs to be segmented and labeled in the bright-field images. However, the ambiguous boundary of bright-field yeast cell images leads to the failure of traditional segmentation algorithms. We propose a segmentation algorithm based on the morphological characteristics of yeast cells. Seed points are first identified along the cell contour and then connected by an edge tracing approach. In this way, "ill-detected" noise points are removed so that edges of yeast cells can be successfully extracted in bright-field images with sparsely distributed cells. In densely packed images, yeast cells with normal morphology can also be correctly segmented and labeled.
酵母细胞表型分析需要高通量成像和对大量图像数据的自动分析。首先,需要在明场图像中对每个细胞进行分割和标记。然而,明场酵母细胞图像的不明确边界导致传统分割算法的失败。我们提出了一种基于酵母细胞形态特征的分割算法。首先沿着细胞轮廓识别种子点,然后通过边缘跟踪方法连接。通过这种方式,可以去除“检测不良”的噪声点,从而成功提取明场图像中稀疏分布细胞的酵母细胞边缘。在密集的图像中,具有正常形态的酵母细胞也可以正确分割和标记。