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White Blood Cell Detection and Counting Using PSO
Ali Mohammand Nickfarjam1,2, Farahnaz Hosseini3, Zahra Sadat Kia4
1Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.
Studies in Health Technology and Informatics
|July 1, 2025
Summary
This study introduces an automated method using Particle Swarm Optimization (PSO) for accurate white blood cell (WBC) detection in blood images. The novel approach significantly improves WBC counting efficiency and accuracy compared to traditional methods.
Area of Science:
- Medical Diagnostics
- Computational Biology
- Image Analysis
Background:
- Accurate white blood cell (WBC) detection and counting are vital for diagnosing diseases.
- Manual methods for WBC analysis are inefficient and prone to errors.
- Automated techniques are needed for precise and rapid hematological analysis.
Purpose of the Study:
- To develop an automated method for enhancing the accuracy and efficiency of white blood cell (WBC) detection.
- To leverage Particle Swarm Optimization (PSO) for optimizing WBC identification in microscopic blood images.
Main Methods:
- Image preprocessing: conversion to HSV color space.
- Segmentation: binarization and connected component analysis.
- Optimization: application of Particle Swarm Optimization (PSO) to refine detection and eliminate false positives.
Main Results:
- The proposed PSO-based method achieved a superior accuracy of 61.76% in WBC detection.
- Demonstrated improved performance compared to conventional WBC detection techniques.
- Validated on a dataset of microscopic blood images.
Conclusions:
- The developed method effectively enhances WBC detection and counting accuracy.
- Particle Swarm Optimization offers a valuable tool for automated hematological analysis.
- This approach presents a significant advancement for automated medical diagnostics.
