Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography.

IEEE journal of translational engineering in health and medicine·2026

Same author

Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models.

Frontiers in artificial intelligence·2026

Same author

Stomatognathic Diseases Reveal Bidirectional Link Between Diabetes Mellitus and Coronary Artery Calcium: A Cross-Sectional Study Using Multi-Way Array Analysis.

Health science reports·2025

Same author

Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.

Scientific reports·2024

Same author

Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots.

Chaos (Woodbury, N.Y.)·2024

Same author

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Current oncology (Toronto, Ont.)·2024

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Analysis and recognition of touching cell images based on morphological structures.

Donggang Yu¹, Tuan D Pham, Xiaobo Zhou

¹School of Design, Communication and Information Technology, The University of Newcastle, Callaghan, NSW 2308, Australia. dyu2008@gmail.com

Computers in Biology and Medicine

|December 17, 2008

Summary

This study introduces a novel method for automated cell segmentation in fluorescence microscopy, crucial for high-content screening. The approach accurately identifies and reconstructs touching cells, improving image analysis efficiency.

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

Published on: June 27, 2022

Related Experiment Videos

Last Updated: Jun 27, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

Published on: June 27, 2022

Area of Science:

Cell biology
Biomedical imaging
Computational analysis

Background:

Automated analysis of cell-nuclear phases in fluorescence microscopy is vital for high-content screening.
Segmenting and reconstructing individual cells from touching cell images presents a significant challenge in automated imaging.

Purpose of the Study:

To present a novel method for recognizing morphological structural models of touching cells.
To detect segmentation points, determine cell counts, and reconstruct segmented cells from touching cell images.

Main Methods:

The method is based on conceptual frameworks of morphological structures.
It establishes a series of structural points and their morphological relationships for analysis.
Key steps include recognizing cell models, detecting segmentation points, and reconstructing cells.

Main Results:

The developed method efficiently analyzes and recognizes touching cell images.
It successfully determines the number of segmented cells within touching cell images.
The technique allows for the reconstruction of segmented cells and analysis of their data.

Conclusions:

The new method offers an efficient solution for the analysis and recognition of touching cell images in high-content screening.
Accurate cell segmentation and reconstruction are demonstrated, enhancing automated imaging capabilities.
The approach based on morphological structures proves effective for complex cell image analysis.