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Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: May 19, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Semiautomatic white blood cell segmentation based on multiscale analysis.

L B Dorini, R Minetto, N J Leite

    IEEE Journal of Biomedical and Health Informatics
    |August 3, 2012
    PubMed
    Summary

    This study introduces novel methods for segmenting white blood cell (WBC) nucleus and cytoplasm. These techniques enhance accuracy in automated cell counting for disease diagnosis.

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    Area of Science:

    • Medical image analysis
    • Computational pathology
    • Biomedical engineering

    Background:

    • Accurate segmentation of white blood cells (WBCs) is crucial for automated differential counting.
    • Existing segmentation methods face challenges with varying cell appearance and image quality.

    Purpose of the Study:

    • To develop and evaluate novel methods for segmenting the nucleus and cytoplasm of WBCs.
    • To improve the accuracy of automated differential counting for disease diagnosis.

    Main Methods:

    • Application of the Self-Dual Multiscale Morphological Toggle (SMMT) operator for image simplification and contour regularization.
    • Segmentation of the nucleus using SMMT-enhanced watershed transform and Level Set methods.
    • Identification of the cytoplasm region through granulometric analysis and morphological transformations.

    Main Results:

    • SMMT preprocessing proved essential for accurate nucleus segmentation.
    • Proposed methods demonstrated promising segmentation and classification results across diverse images.
    • Successful application to a large dataset with varying cell morphology and image quality.

    Conclusions:

    • The developed methods offer a robust approach to WBC segmentation.
    • These techniques provide a foundation for improved automated differential counting.
    • Further research is encouraged to explore the full potential of these novel methods.