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Fission Yeast as a Platform for Antibacterial Drug Screens Targeting Bacterial Cytoskeleton Proteins
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Image processing and classification algorithm for yeast cell morphology in a microfluidic chip.

Bo Yang Yu1, Caglar Elbuken, Carolyn L Ren

  • 1University of Waterloo, Department of Mechanical and Mechatronics Engineering, Waterloo, Ontario, N2L 3G1, Canada.

Journal of Biomedical Optics
|July 5, 2011
PubMed
Summary
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This study developed an automated computer algorithm to classify yeast cell cycle phases using bud size from microscopic images. The method accurately identifies yeast morphology even with noisy, low-contrast images.

Area of Science:

  • Computational Biology
  • Microscopy Image Analysis
  • Yeast Cell Biology

Background:

  • Accurate yeast cell cycle phase identification is crucial for morphological studies.
  • Manual classification of yeast cell morphology is time-consuming and subjective.
  • Microfluidic environments present unique challenges for image analysis.

Purpose of the Study:

  • To develop and evaluate a computer-based algorithm for automatic classification of yeast cell cycle phases.
  • To extract and utilize morphological features like bud size for classification.
  • To assess the performance of machine learning classifiers under varying image conditions.

Main Methods:

  • Image enhancement techniques to reduce background noise.
  • Development of a robust segmentation algorithm to extract geometrical features (compactness, axis ratio, bud size).

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  • Comparison of linear support vector machine, distance-based classification, and k-nearest-neighbor algorithms for classification.

    • Main Results:

    • The algorithm successfully extracts key morphological features from yeast cell images.
    • Machine learning classifiers demonstrated effectiveness in classifying yeast cell cycle phases.
    • The system showed robust performance despite variations in illumination and focus.

    Conclusions:

    • Automated classification of yeast cells based on morphology is feasible.
    • The developed algorithm can accurately classify yeast cells even with noisy and low-contrast images.
    • This approach offers a consistent and efficient method for yeast cell cycle analysis.