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20 November 2018 Segmentation of yeast cell’s bright-field image with an edge-tracing algorithm
Linbo Wang, Simin Li, Zhenglong Sun, Gang Wen, Fan Zheng, Chuanhai Fu, Hui Li
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Journal of Biomedical Optics, Vol. 23, Issue 11, 116503 (November 2018). https://doi.org/10.1117/1.JBO.23.11.116503
Abstract
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Linbo Wang, Simin Li, Zhenglong Sun, Gang Wen, Fan Zheng, Chuanhai Fu, and Hui Li "Segmentation of yeast cell’s bright-field image with an edge-tracing algorithm," Journal of Biomedical Optics 23(11), 116503 (20 November 2018). https://doi.org/10.1117/1.JBO.23.11.116503
Received: 5 July 2018; Accepted: 17 October 2018; Published: 20 November 2018
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Cited by 9 scholarly publications.
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KEYWORDS
Image segmentation
Yeast
Image processing algorithms and systems
Algorithm development
Lithium
Detection and tracking algorithms
Edge detection
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