Open Access

ARTICLE

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Shouming Hou¹, Chaolan Jia¹, Kai Li¹, Liya Fan², Jincheng Guo^3,*, Mackenzie Brown^4,*

1 School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China
2 City College, Xi’an Jiaotong University, Xi’an, 710018, China
3 Department of Thoracic Surgery, Jiaozuo Second People’s Hospital, Jiaozuo, 454000, China
4 School of Engineering, Edith Cowan University, Joondalup, 6027, Australia

* Corresponding Authors: Jincheng Guo. Email: ; Mackenzie Brown. Email:

(This article belongs to this Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)

Computer Modeling in Engineering & Sciences 2022, 132(1), 81-94. https://doi.org/10.32604/cmes.2022.019006

Received 29 August 2021; Accepted 09 December 2021; Issue published 02 June 2022

Abstract

Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID-19, Kaggle-COVID-19, and COVID-Chestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the F-Score of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

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Keywords

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