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

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Published on: November 30, 2022

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation.

Donglin Zhu¹, Jiaying Shen¹, Yangyang Zheng¹

¹School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.

Computers in Biology and Medicine

|May 14, 2024

Summary

This summary is machine-generated.

This study introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) to improve COVID-19 medical image segmentation. IPSOsono enhances thresholding accuracy, overcoming limitations of traditional methods for better diagnostic insights.

Keywords:

COVID-19 Global optimization Particle swarm optimization Threshold segmentation

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Published on: December 19, 2020

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Optimization

Background:

Medical image analysis is crucial for COVID-19 research, with image segmentation being a key preliminary step.
Traditional thresholding methods for image segmentation face challenges in optimal threshold selection and efficiency.
Existing algorithms struggle with stagnation and local optima in complex optimization tasks.

Purpose of the Study:

To introduce an enhanced optimization algorithm, Inner-based multi-strategy particle swarm optimization (IPSOsono), for medical image segmentation.
To improve the accuracy and efficiency of threshold image segmentation specifically for COVID-19 medical images.
To address the limitations of traditional thresholding and existing optimization techniques in medical image analysis.

Main Methods:

Developed IPSOsono by incorporating a novel dynamic oscillatory weight from the Particle Swarm Optimization variant for single-objective numerical optimization (PSOsono).
Implemented random updates of historical optimal positions within the particle swarm to mitigate stagnation and local optima.
Introduced an inner selection learning mechanism for dynamic refinement of the global optimal solution during position updates.

Main Results:

PSOscono demonstrated superior optimization capability compared to recent algorithms in the CEC 2013 benchmark test.
IPSOsono achieved more prominent segmentation capability in Minimum Cross Entropy thresholding for COVID-19 medical images compared to other algorithms.
The algorithm showed good generalization across 6 CT images, validating its practical applicability.

Conclusions:

IPSOsono is an effective and feasible optimization algorithm for enhancing threshold image segmentation.
The proposed method offers significant improvements over existing algorithms for COVID-19 medical image analysis.
PSOscono's practical utility is confirmed by its strong performance in real-world medical imaging scenarios.