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Related Experiment Video

Updated: Jul 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

White blood cell image segmentation using on-line trained neural network.

Fang Yi1, Zheng Chongxun, Pan Chen

  • 1Student Member, IEEE, Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049 China.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
Summary

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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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This study presents a fast white blood cell (WBC) image segmentation method using a neural network. Optimized training significantly reduces dataset size and processing time while maintaining accuracy.

Area of Science:

  • Medical image analysis
  • Computational biology
  • Artificial intelligence in healthcare

Background:

  • Accurate segmentation of white blood cells (WBCs) is crucial for hematological diagnoses.
  • Traditional neural network training for image segmentation can be computationally intensive and require large datasets.
  • Challenges include achieving high accuracy with reduced training data and faster processing.

Purpose of the Study:

  • To develop a computationally efficient scheme for white blood cell (WBC) image segmentation.
  • To reduce the size of the training dataset required for neural network implementation.
  • To accelerate the training process and improve convergence speed.

Main Methods:

  • Implementation of an on-line trained neural network for WBC image segmentation.

Related Experiment Videos

Last Updated: Jul 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Utilizing a pre-selecting technique based on mean shift algorithm and uniform sampling for training set initialization.
  • Employing Particle Swarm Optimization (PSO) for neural network training to enhance convergence and avoid local optima.

    • Main Results:

    • Significant reduction in training set size compared to traditional methods.
    • Substantial decrease in overall running time for image segmentation.
    • Maintained compatible image segmentation accuracy with optimized training approaches.

    Conclusions:

    • The proposed on-line trained neural network with PSO and pre-selection offers an efficient WBC image segmentation solution.
    • This method effectively reduces computational costs associated with training data and processing time.
    • It provides a viable alternative for rapid and accurate medical image analysis in hematology.