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Updated: Jun 10, 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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Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization.

Chanho Jung1, Changick Kim

  • 1Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-732, Korea. peterjung@kaist.ac.kr

IEEE Transactions on Bio-Medical Engineering
|July 27, 2010
PubMed
Summary
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This study introduces a new watershed-based method for segmenting cervical and breast cell nuclei. The novel approach improves accuracy in cell image analysis compared to existing watershed techniques.

Area of Science:

  • Biomedical Image Analysis
  • Computational Pathology
  • Cell Biology

Background:

  • Accurate segmentation of cell nuclei is crucial for diagnosing cervical and breast cancers.
  • Existing watershed-based methods struggle with clustered nuclei, impacting diagnostic accuracy.

Purpose of the Study:

  • To develop a novel watershed-based method for precise segmentation of clustered cervical and breast cell nuclei.
  • To improve the accuracy of cell image analysis through advanced segmentation techniques.

Main Methods:

  • Formulating nuclei segmentation as an optimization problem using a priori shape knowledge.
  • Applying distance transform and H-minima transform for marker extraction and optimal segmentation.
  • Introducing a size-invariant distortion evaluation function and ellipsoidal modeling for contour refinement.

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Related Experiment Videos

Last Updated: Jun 10, 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

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Main Results:

  • The proposed method achieves more accurate segmentation of clustered nuclei compared to current state-of-the-art watershed-based techniques.
  • Ellipsoidal modeling enhances the analysis of nuclei contours for improved diagnostic potential.

Conclusions:

  • The novel watershed-based method offers superior performance for segmenting cell nuclei in microscopic images.
  • This advancement holds promise for more accurate and reliable automated analysis in cervical and breast cancer diagnostics.