A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm

The coronavirus disease 2019 (COVID-19) is a substantial threat to people’s lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affecte...

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Autores principales:	Shou-Ming Hou, Chao-Lan Jia, Ming-Jie Hou, Steven L. Fernandes, Jin-Cheng Guo
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a1063cc8cc424fb095d9d473f173535b
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spelling	oai:doaj.org-article:a1063cc8cc424fb095d9d473f173535b2021-11-08T02:36:20ZA Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm1748-671810.1155/2021/5208940https://doaj.org/article/a1063cc8cc424fb095d9d473f173535b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5208940https://doaj.org/toc/1748-6718The coronavirus disease 2019 (COVID-19) is a substantial threat to people’s lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K-means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.Shou-Ming HouChao-Lan JiaMing-Jie HouSteven L. FernandesJin-Cheng GuoHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Shou-Ming Hou Chao-Lan Jia Ming-Jie Hou Steven L. Fernandes Jin-Cheng Guo A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
description	The coronavirus disease 2019 (COVID-19) is a substantial threat to people’s lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K-means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.
format	article
author	Shou-Ming Hou Chao-Lan Jia Ming-Jie Hou Steven L. Fernandes Jin-Cheng Guo
author_facet	Shou-Ming Hou Chao-Lan Jia Ming-Jie Hou Steven L. Fernandes Jin-Cheng Guo
author_sort	Shou-Ming Hou
title	A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
title_short	A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
title_full	A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
title_fullStr	A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
title_full_unstemmed	A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm
title_sort	study on weak edge detection of covid-19’s ct images based on histogram equalization and improved canny algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a1063cc8cc424fb095d9d473f173535b
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A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm

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