Related Experiment Video
Updated: Jun 6, 2026
09:11
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
Published on: January 27, 2023
Classification of apoptosis using advanced clustering techniques on digital microscopic images.
S K Tasoulis1, C N Doukas, I Maglogiannis
1Department of Computer Science and Biomedical Informatics, University of Central Greece, Papassiopoulou 2-4, Lamia, 35100, Greece. stas@uc.gr
Summary
This study introduces a novel method for characterizing apoptosis (programmed cell death) by quantifying and detecting apoptotic cells in microscopic images using active contours and advanced clustering algorithms.
Area of Science:
- Biotechnology
- Medical Imaging
- Computational Biology
Background:
- Programmed cell death (apoptosis) is crucial for biological processes.
- Apoptosis is strongly linked to diseases such as cancer and HIV.
Purpose of the Study:
- To present an innovative method for apoptosis characterization.
- To enable accurate quantification and detection of apoptotic cells.
Main Methods:
- Utilizing active contours for apoptotic cell detection and quantification.
- Applying data mining techniques, specifically Principal Component Analysis-driven clustering algorithms, to digital microscopic images.
Main Results:
- Successful characterization of apoptosis using the proposed method.
- Demonstrated effectiveness of a novel clustering algorithm for real-world data.
Conclusions:
- The developed method offers an effective approach for apoptosis analysis.
- The study highlights the utility of advanced clustering in biomedical image analysis.
