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Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Related Experiment Video

Updated: Jul 14, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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Ontology-based lymphocyte population description using mathematical morphology on colour blood images.

J Angulo1, J Klossa, G Flandrin

  • 1Centre de Morphologie Mathématique, Ecole des Mines de Paris, Fontainebleau, France. jesus.angulo@ensmp.fr

Cellular and Molecular Biology (Noisy-Le-Grand, France)
|June 5, 2007
PubMed
Summary

This study introduces an image-based approach using mathematical morphology to objectively describe lymphocyte populations from peripheral blood smear images. This method aims to enhance diagnostic accuracy by combining automation with medical expertise.

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Area of Science:

  • Hematology
  • Medical Imaging
  • Computational Biology

Background:

  • Microscopic examination of peripheral blood smears is crucial for diagnosing various diseases, including leukaemic disorders.
  • Modern technologies complement, but do not replace, traditional cytomorphologic analysis.
  • An objective and standardized method for lymphocyte population description is needed.

Purpose of the Study:

  • To develop an automated, image-based system for describing lymphocyte populations.
  • To create an objective framework using mathematical morphology and a specific ontology.
  • To integrate automated analysis with human medical expertise for improved diagnostics.

Main Methods:

  • Utilizing mathematical morphology tools for processing peripheral blood colour images.
  • Developing an ontology-based framework for lymphocyte classification.
  • Creating image processing and data classification algorithms.
  • Generating representative image databases for training and validation.

Main Results:

  • Demonstrated high-performance of the image-based approach in describing lymphocyte populations.
  • Successfully developed algorithms for image processing and data classification.
  • Validated the system's effectiveness through comparison with human expertise.

Conclusions:

  • The developed image-based approach offers an objective and understandable description of lymphocyte populations.
  • This method successfully reconciles automation with medical expertise, creating a synergistic diagnostic tool.
  • The system has the potential to enhance the efficiency and accuracy of diagnosing hematologic disorders.