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John L. Dart Library
Closed for Maintenance
Phone: (843) 722-7550
West Ashley Library
9 a.m. - 5 p.m.
Phone: (843) 766-6635
Folly Beach Library
9 a.m. - 2 p.m.
*open the 2nd and 4th Saturday
*open the 2nd and 4th Saturday
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
Wando Mount Pleasant Library
9 a.m. - 5 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 1 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
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9 a.m. - 5 p.m.
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Advanced COVID-19 CT Image Segmentation Using a Hybrid Undecimated Wavelet Transform, Fuzzy Clustering, and Anisotropic Diffusion Approach.
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- Author(s): Larbi, Messaouda1; Naimi, Hilal2 ; Bourennane, Mohammed2
- Source:
Traitement du Signal. Jun2023, Vol. 40 Issue 3, p1045-1054. 10p.- Subject Terms:
- Source:
- Additional Information
- Abstract: Early detection of Coronavirus Disease 2019 (COVID-19), an infectious disease caused by the SARS-CoV-2 virus, is crucial in minimizing the risk of mortality and limiting its spread, particularly among asymptomatic individuals. Computed tomography (CT) scans of the chest are commonly employed for diagnosing this condition, necessitating the development of segmentation techniques for analyzing these images effectively. However, segmenting COVID-19 CT images poses considerable challenges due to the indistinct boundaries between gray and white matter, as well as the homogeneous and ambiguous structures within the regions. To address these issues, we propose a hybrid approach that combines Undecimated Wavelet Transform (UWT), Fuzzy Clustering (FC), and Anisotropic Diffusion Filter (ADF). Our method involves utilizing UWT to denoise CT images in the frequency domain, followed by an advanced fuzzy clustering technique based on texture features and local gray value entropy for autonomous segmentation of CT images. The segmented images are then processed with ADF to eliminate uncertainty and noise. The performance of our proposed method was evaluated visually and through similarity measurements using an open-source dataset. A comparative analysis with alternative segmentation methods was conducted using multiple metrics, including Dice, Jaccard, Precision, Accuracy, Sensitivity, F-measure, MCC, and Specificity. Our results demonstrate that the proposed hybrid approach significantly enhances the detection of COVID-19 from CT images. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Traitement du Signal is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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