Related Experiment Video
Updated: Jul 11, 2026
08:05
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
14.3K
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.
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.
Related Concept Videos
01:37Super-resolution Fluorescence Microscopy
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
01:13Radiological Investigation III: Pulmonary Angiogram and PET Scan
Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...