Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

1.2K
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
1.2K
Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

1.3K
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
1.3K
Bacterial Growth Curve01:28

Bacterial Growth Curve

1.9K
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
1.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dataset for multi-perspective traffic video analysis.

Scientific data·2026
Same author

High entropy alloy property predictions using a transformer-based language model.

Scientific reports·2025
Same author

Outdoor THz fading modeling by means of gaussian and gamma mixture distributions.

Scientific reports·2023
Same author

Validation of a web-based distance visual acuity test.

Journal of cataract and refractive surgery·2023
Same author

Development and Validation of the First Smart TV-Based Visual Acuity Test: A Prospective Study.

Healthcare (Basel, Switzerland)·2022
Same author

Impact of personality on the decision process and on satisfaction rates in pseudophakic presbyopic correction.

Journal of cataract and refractive surgery·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 22, 2025

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
11:30

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software

Published on: July 24, 2021

4.1K

Measurement and Modeling of Microbial Growth Using Timelapse Video.

Konstantinos Delibasis1, Ifigenia Basanou1, Alexandros-Apostolos A Boulogeorgos2

  • 1Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, Greece.

Sensors (Basel, Switzerland)
|May 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost timelapse method for tracking microbial growth, developing an image analysis algorithm to estimate fungal populations and a novel logistic function model for accurate growth curve analysis.

Keywords:
image-based measurement,microbial growth model, timelapse video

More Related Videos

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

5.3K
Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy
07:31

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

Published on: July 28, 2011

43.2K

Related Experiment Videos

Last Updated: Dec 22, 2025

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
11:30

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software

Published on: July 24, 2021

4.1K
High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

5.3K
Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy
07:31

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

Published on: July 28, 2011

43.2K

Area of Science:

  • Microbiology
  • Computational Biology
  • Image Analysis

Background:

  • Timelapse videos offer a low-cost, simple method for studying microbial colony development.
  • Investigating the growth dynamics of Candida SPP. is crucial for understanding fungal behavior.

Purpose of the Study:

  • To develop a simple experimental setup for creating timelapse videos of Candida SPP. growth.
  • To propose a computational algorithm for estimating microbial populations and generating growth curves.
  • To introduce and validate a novel mathematical model for microbial population evolution.

Main Methods:

  • A straightforward experimental setup for periodic snapshot acquisition of petri dishes.
  • An image processing algorithm to estimate microbial populations and extract experimental growth curves.
  • A new logistic function-based model for population evolution, with parameter estimation and comparison to conventional methods.

Main Results:

  • The proposed algorithm accurately estimates microbial populations and generates time-evolution curves.
  • The novel logistic function model effectively describes microbial population dynamics.
  • Increasing the area size for image analysis enhances curve smoothness, signal-to-noise ratio, and parameter estimation accuracy.

Conclusions:

  • The developed timelapse method and image analysis algorithm provide a robust tool for microbial growth studies.
  • The novel logistic function model offers an accurate approach for modeling microbial population evolution.
  • Image analysis parameterization significantly impacts the reliability and accuracy of microbial growth estimations.