COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm. (January 2023)

• Record Type: Journal Article
• Authors: Wang, Guowei; Guo, Shuli; Han, Lina; Zhao, Zhilei; Song, Xiaowei
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: COVID-19 -- Ground-glass opacity -- Fuzzy c-means clustering -- Random walk -- Snowfall model -- Markov random field; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104159
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors. Highlights: The adaptive spatial information MRF based FCM CT image clustering algorithm is proposed. The geodesic distance is added to redefine the weight function of the random walk. The proportion of gray feature and spatial information is adaptively calculated in the weight. The adaptive Gaussian kernel of snowfall model is proposed to reduce the noise of CT image.