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. 2023;14(7):9217-9232.
doi: 10.1007/s12652-022-04425-4. Epub 2022 Oct 21.

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

Osama S Faragallah  1 , Heba M El-Hoseny  2 , Hala S El-Sayed  3
Affiliations

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

Osama S Faragallah et al. J Ambient Intell Humaniz Comput. 2023.

Abstract

In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation.

Keywords: COVID-19; Local Laplacian filter; MCFO; SLIC; Super-pixels.

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Figures

Fig. 1
Fig. 1
The proposed MCFO-SLIC scheme with local Laplacian filter
Fig. 2
Fig. 2
Accuracy measurement of the proposed MCFO-based SLIC segmentation algorithm and thresholding segmentation
Fig. 3
Fig. 3
F-Measure quality metric of the proposed MCFO-based SLIC segmentation algorithm and thresholding segmentation
Fig. 4
Fig. 4
Jaccard quality metric of the proposed MCFO-based SLIC segmentation algorithm and thresholding segmentation
Fig. 5
Fig. 5
Boundary recall metric of the proposed MCFO-based SLIC segmentation algorithm and thresholding segmentation
See this image and copyright information in PMC

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