Linbo Wang, Simin Li, Zhenglong Sun, Gang Wen, Fan Zheng, Chuanhai Fu, Hui Li
Journal of Biomedical Optics, Vol. 23, Issue 11, 116503, (November 2018) https://doi.org/10.1117/1.JBO.23.11.116503
TOPICS: Image segmentation, Yeast, Image processing algorithms and systems, Algorithm development, Lithium, Detection and tracking algorithms, Edge detection, Image analysis, Genetics, Sun
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.