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Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm.

Yazan M Alomari1, Siti Norul Huda Sheikh Abdullah1, Raja Zaharatul Azma2

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Area of Science:

  • Medical Imaging Analysis
  • Hematology Automation
  • Computational Pathology

Background:

  • Accurate blood cell counting is crucial for diagnosing diseases like malaria and leukemia.
  • Manual cell counting is labor-intensive, time-consuming, and prone to inaccuracies.
  • Automated methods are needed to enhance speed and precision in hematological analysis.

Purpose of the Study:

  • To develop and validate an automated algorithm for segmenting and counting red blood cells (RBCs) and white blood cells (WBCs).
  • To improve the efficiency and accuracy of blood cell analysis compared to manual methods.
  • To provide a reliable tool for hematologists in disease diagnosis.

Main Methods:

  • An iterative structured circle detection algorithm was employed for cell segmentation and counting.
  • Specific preprocessing steps and thresholding were utilized for separating and analyzing RBCs and WBCs.
  • Modifications to a basic circle detection algorithm addressed initialization, irregular shapes, optimal circle selection, dynamic iterations, and runtime.

Main Results:

  • The proposed method demonstrated high accuracy in automated blood cell counting.
  • Average accuracy achieved was 95.3% for RBCs and 98.4% for WBCs.
  • Quantitative validation using Precision, Recall, and F-measurement confirmed segmentation accuracy.

Conclusions:

  • The developed iterative structured circle detection algorithm offers an accurate and efficient automated solution for blood cell segmentation and counting.
  • This automated approach significantly improves upon the limitations of manual cell counting.
  • The method shows strong potential for aiding in the faster and more accurate diagnosis of hematological conditions.