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Related Experiment Video

Updated: Jul 11, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.

Shouvik Chakraborty¹, Kalyani Mali¹

¹Department of Computer Science and Engineering, University of Kalyani, India.

Applied Soft Computing

|September 20, 2022

Summary

A new unsupervised segmentation method, SUFEMO, aids in early COVID-19 diagnosis from chest CT scans. This approach improves accuracy and efficiency for medical professionals, potentially reducing virus spread.

Keywords:

Biomedical image segmentation COVID-19 Radiological image elucidation Superpixel Unsupervised clustering

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Last Updated: Jul 11, 2026

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Early detection of COVID-19 is crucial for timely treatment and controlling viral spread.
Current diagnostic methods may benefit from enhanced image analysis techniques.

Purpose of the Study:

To introduce a novel unsupervised segmentation method for COVID-19 detection in radiological images.
To improve the speed and accuracy of early COVID-19 diagnosis for medical experts.
To reduce the computational burden associated with analyzing large medical image datasets.

Main Methods:

Developed SUFEMO (Superpixel based Fuzzy Electromagnetism-like Optimization), integrating superpixels, type-2 fuzzy logic, and an optimized Electromagnetism-like algorithm.
Modified the Electromagnetism-like algorithm for cluster center updates, independent of initial center selection.
Addressed noise sensitivity in superpixel formation using gradient image analysis and adapted the fuzzy objective function.

Main Results:

Evaluated on 310 chest CT scans, SUFEMO demonstrated superior qualitative and quantitative performance compared to state-of-the-art methods.
Achieved strong cluster validity index scores (e.g., Davies-Bouldin index of 1.812008792).
Exhibited a faster convergence rate and proven real-life applicability for initial COVID-19 patient filtering.

Conclusions:

SUFEMO offers an effective and efficient solution for unsupervised segmentation in COVID-19 diagnosis.
The method enhances diagnostic capabilities for physicians and medical technologists.
SUFEMO's performance and applicability support its use in clinical settings for early COVID-19 detection.