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Related Experiment Video

Updated: Jun 25, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
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SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Segmentation of clustered nuclei with shape markers and marking function.

Jierong Cheng1, Jagath C Rajapakse

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 638798, Singapore. jrcheng@ntu.edu.sg

IEEE Transactions on Bio-Medical Engineering
|March 11, 2009
PubMed
Summary

This study introduces a new method for separating clustered cell nuclei in microscopy images. The approach improves segmentation accuracy by 6-7% compared to existing techniques.

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Area of Science:

  • Cellular imaging
  • Biotechnology
  • Image analysis

Background:

  • Accurate segmentation of clustered nuclei is crucial for quantitative cell analysis.
  • Existing methods struggle with precise separation of overlapping nuclei in microscopy images.

Purpose of the Study:

  • To develop an improved algorithm for separating clustered nuclei in fluorescence microscopy images.
  • To enhance the accuracy of cell segmentation using novel image processing techniques.

Main Methods:

  • A watershed-like algorithm incorporating shape markers and a novel marking function.
  • Extraction of shape markers via adaptive H-minima transform.
  • Development of an outer distance transform-based marking function for precise nuclei separation.

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Related Experiment Videos

Last Updated: Jun 25, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion
09:03

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Published on: April 13, 2019

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Main Results:

  • Quantitative performance evaluation on synthetic images demonstrating method efficacy.
  • Achieved 6%-7% improvement in segmentation accuracy on mouse neuronal and Drosophila cellular images.
  • Demonstrated superior performance compared to existing nuclei segmentation approaches.

Conclusions:

  • The proposed method effectively separates clustered nuclei with enhanced accuracy.
  • This technique offers a significant advancement for automated cell analysis in biological research.
  • The algorithm provides reliable nuclei segmentation for diverse cellular imaging applications.