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Updated: Jun 2, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Published on: January 27, 2023

Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake.

Byoung Chul Ko1, Ja-Won Gim, Jae-Yeal Nam

  • 1Department of Computer Engineering, Keimyung University, Shindang-dong Dalseo-gu, Daegu, Republic of Korea. niceko@kmu.ac.kr

Micron (Oxford, England : 1993)
|May 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for segmenting white blood cell (WBC) images. The technique accurately identifies nuclei and cytoplasm in stained WBCs using clustering and snake-based approaches.

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Last Updated: Jun 2, 2026

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Image Analysis

Background:

  • Accurate segmentation of white blood cells (WBCs) is crucial for hematological diagnoses.
  • Existing segmentation methods often struggle with variations in staining and cell morphology.
  • Automated segmentation can improve efficiency and consistency in blood cell analysis.

Purpose of the Study:

  • To develop and evaluate a new automated method for segmenting stained white blood cell images.
  • To accurately segment both the nuclei and cytoplasm of various WBC types.
  • To improve the precision of WBC image analysis through advanced segmentation techniques.

Main Methods:

  • Nuclei segmentation using probability maps, mean-shift clustering, and stepwise merging.
  • Cytoplasm segmentation employing morphological operations, edge detection, and Gradient Vector Flow (GVF) snakes.
  • Development of specific rules for merging clusters and removing noise/boundary edges.

Main Results:

  • The proposed method achieved accurate segmentation for most tested WBC types.
  • Demonstrated effective separation of nuclei and cytoplasm based on color and texture features.
  • Successfully applied stepwise merging and GVF snake deformation for robust segmentation.

Conclusions:

  • The novel segmentation algorithm provides accurate results for stained WBC images.
  • The combined approach of mean-shift clustering and GVF snakes offers a promising solution for automated WBC analysis.
  • This method has the potential to enhance diagnostic capabilities in hematology.