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Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

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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.
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Structure and Function of Leukocytes01:21

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An adult in good health typically has between 4,500 and 11,000 leukocytes, or white blood cells, per microliter of blood, which constitutes about 1% of the total blood volume. Unlike red blood cells, white blood cells contain a nucleus and other cellular organelles but do not have hemoglobin. Most white blood cells reside in connective tissues, particularly in lymphatic organs such as the lymph nodes, with only a small fraction present in circulating blood.
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Related Experiment Video

Updated: Mar 26, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
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Efficient leukocyte segmentation and recognition in peripheral blood image.

Syed H Shirazi1, Arif Iqbal Umar1, Saeeda Naz1,2

  • 1Hazara University, Mansehra, Pakistan.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|February 3, 2016
PubMed
Summary

This study developed an automated system for leukocyte segmentation and classification, overcoming challenges with overlapping cells. The system achieved high accuracy in identifying different white blood cell types, improving diagnostic efficiency.

Keywords:
Leukocyteblood cell segmentationcurveletleukocyte classificationmicroscope image analysis

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Hematology

Background:

  • Manual blood cell counting is time-consuming and prone to human error.
  • Automated analysis of leukocytes (white blood cells) offers improved accuracy and efficiency for disease diagnosis.
  • Leukocyte segmentation in complex cell images remains a significant challenge in automated blood analysis.

Purpose of the Study:

  • To develop an efficient system for leukocyte cell segmentation and classification.
  • To address the difficulties in accurately segmenting and identifying individual white blood cells.

Main Methods:

  • Image enhancement using Wiener filter and Curvelet transform to reduce noise and false edges.
  • Segmentation and boundary detection employing entropy filter, thresholding, and mathematical morphology.
  • Classification of leukocyte subtypes using a back-propagation neural network.

Main Results:

  • Successfully overcame the challenge of overlapping cells in segmentation.
  • Achieved high classification accuracies: 100% for basophils, 96.15% for eosinophils, 92.30% for monocytes, 96.15% for neutrophils, and 92.30% for lymphocytes.

Conclusions:

  • The developed system provides an effective solution for automated leukocyte segmentation and classification.
  • The method significantly improves accuracy and efficiency in analyzing blood cell images.
  • This approach has the potential to enhance the diagnosis of various blood-related diseases.