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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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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Fast leukocyte image segmentation using shadowed sets.

Subrajeet Mohapatra1, Dipti Patra, Kundan Kumar

  • 1Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, India. subrajeets@gmail.com

International Journal of Computational Biology and Drug Design
|March 23, 2012
PubMed
Summary

A new Shadowed C-means (SCM) clustering method accurately segments leukocytes in blood images. This automated approach improves diagnostic accuracy for hematological diseases like leukemia.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Hematology

Background:

  • Automated image-based systems for hematological disease recognition rely heavily on accurate leukocyte image segmentation.
  • Current segmentation methods face challenges, particularly in pathological imaging, impacting diagnostic accuracy in automated cytology.

Purpose of the Study:

  • To introduce a novel Shadowed C-means (SCM) clustering algorithm for improved leukocyte segmentation in blood microscopic images.
  • To enable accurate feature extraction of leukocyte nucleus and cytoplasm for acute leukemia detection.

Main Methods:

  • A Shadowed C-means (SCM) clustering approach was developed for segmenting leukocytes.
  • The algorithm was tested on stained blood microscopic images, evaluating its performance in the presence of outliers.

Main Results:

  • The proposed SCM algorithm demonstrated robust and fast segmentation of leukocytes.
  • SCM achieved acceptable segmentation performance without requiring parameter tuning, outperforming standard clustering techniques.

Conclusions:

  • The novel SCM clustering method offers a robust and efficient solution for leukocyte segmentation in microscopic blood images.
  • This technique has the potential to enhance diagnostic accuracy in automated hematology, particularly for detecting diseases like acute leukemia.