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Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision.

Julia Borisova1, Ivan V Morshchinin2, Veronika I Nazarova2

  • 1NSS Lab, AI Institute, ITMO University, St. Petersburg 197101, Russia.

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|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost computer vision method for estimating microalgae cell concentration. The technique uses image analysis to accurately count cells, offering an affordable alternative to expensive automated systems.

Keywords:
cell concentrationcell segmentationcomputer visionmicroalgaemicroscopy

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Area of Science:

  • Microbiology
  • Biotechnology
  • Computer Vision

Background:

  • Accurate microalgae cell concentration estimation is vital for various scientific and industrial applications.
  • Traditional methods like hemocytometry are labor-intensive and error-prone.
  • Existing automated systems are often expensive and require substantial training data.

Purpose of the Study:

  • To develop a low-cost, automated method for estimating microalgae cell concentration.
  • To provide an accessible alternative to manual counting and high-end automated systems.
  • To utilize classical computer vision for cell detection and quantification.

Main Methods:

  • Employing the Hough circle transform for cell detection in microscope images of *Chlorella vulgaris*.
  • Calculating cell concentration (cells/mL) using pixel measurements and a conversion factor.
  • Validating the automated method against manual hemocytometer counts.

Main Results:

  • Strong agreement between the proposed method and manual counts (Pearson correlation coefficient = 0.96).
  • A mean percentage difference of 17.96% was observed.
  • Rapid image processing (under 30 seconds per image) with visual interpretability.

Conclusions:

  • The developed system offers an affordable, efficient, and interpretable solution for microalgae cell concentration estimation.
  • It is suitable for laboratories with limited resources, bridging the gap between manual and advanced automated techniques.
  • The method shows potential for adaptation to other microbiological quantification tasks, despite limitations with cell clumping.