Abstract
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.
Highlights
COVID-19 has caused a health crisis worldwide, impacting all sectors of human life [1]
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
In this paper, based on histogram equalization and improved Canny algorithm, we propose an weak edge detection algorithm
Summary
COVID-19 has caused a health crisis worldwide, impacting all sectors of human life [1]. On 11 March 2020, COVID19 was declared as an extremely high-risk disease by the World Health Organization (WHO) [2]. CT can show detailed symptoms of clinical diagnosis in COVID-19, especially for patients with moderate to severe [4]. Many hospitals use this technique to scan the lungs of patients and diagnose the illness. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is determined as the cause of COVID-19 [5]. There are several CT image features of COVID-19, such as single ground-glass shadow, diffuse ground-glass shadow of both lungs, large area consolidation of both lungs, multiple patchy consolidations, and paving stone-like[6].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational and Mathematical Methods in Medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
