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Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.

Osama S Faragallah1, Heba M El-Hoseny2, Hala S El-Sayed3

  • 1Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.

Journal of Ambient Intelligence and Humanized Computing
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Central Force Optimization (MCFO)-based Simple Linear Iterative Clustering (SLIC) algorithm for segmenting COVID-19 infected lung regions in CT scans, improving early detection of small anomalies.

Keywords:
COVID-19Local Laplacian filterMCFOSLICSuper-pixels

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Area of Science:

  • Computer Vision
  • Medical Image Analysis
  • Artificial Intelligence

Background:

  • Super pixel segmentation is crucial for medical image analysis, particularly for identifying subtle abnormalities.
  • The Simple Linear Iterative Clustering (SLIC) algorithm offers robustness and improved boundary recall in image processing.
  • Accurate segmentation of COVID-19 infected lung regions in CT scans is vital for early detection and tracking.

Purpose of the Study:

  • To develop an efficient modified Central Force Optimization (MCFO)-based SLIC segmentation algorithm for COVID-19 detection in chest CT images.
  • To enhance the detection of small infected lung regions, which are challenging for traditional methods.
  • To evaluate the performance of the proposed algorithm against existing thresholding techniques.

Main Methods:

  • Implementation of a modified Central Force Optimization (MCFO) algorithm integrated with the Simple Linear Iterative Clustering (SLIC) method.
  • Application of the MCFO-SLIC algorithm to segment infected lung regions in chest CT scans.
  • Comparative analysis with a standard thresholding segmentation algorithm using various performance metrics.

Main Results:

  • The MCFO-based SLIC algorithm demonstrated superior performance in detecting small infected regions within CT lung scans.
  • Quantitative evaluation showed improved accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard scores compared to thresholding.
  • The proposed method effectively addresses the limitations of traditional techniques in identifying subtle COVID-19 lung abnormalities.

Conclusions:

  • The MCFO-based SLIC segmentation algorithm provides an efficient and effective approach for COVID-19 detection in chest CT images.
  • This method significantly improves the identification of small, potentially critical infected lung areas.
  • The enhanced segmentation capabilities support faster tracking and earlier diagnosis of COVID-19 cases.