A morphology-based radiological image segmentation approach for efficient screening of COVID-19

Abstract

Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely. The optimal size of the structuring element is selected using the Edge Content-based contrast matrix approach. After performing the opening by using the morphological reconstruction operation, further noise is eliminated using the closing-based morphological reconstruction operation. The original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose. It is preferable to manually determine the range of the infected region. If a region is greater than the upper bound then that region will be considered as an exceptional region and processed separately. Three standard metrics MSE, PSNR, and SSIM are used to quantify the outcomes. Both quantitative and qualitative comparisons prove the efficiency and real-life adaptability of this approach. The proposed approach is evaluated with the help of 400 different images and on average, the proposed approach achieves MSE 307.1888625, PSNR 23.7246505, and SSIM 0.831718459. Moreover, the comparative study shows that the proposed approach outperforms some of the standard methods and obtained results are encouraging to support the battle against the COVID-19.

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

“…In this paper, morphological open operation [25] , [26] and closed operation [27] are combined to remove the interference of noise and other isolated small areas according to the characteristics of target and noise.…”

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

“…The lung parenchyma is more obvious after the initial segmentation of CT image through the above optimal threshold iterative method, but the presence of blood vessels and trachea in the image has a great impact on the accurate segmentation of the lung area. Therefore, the processed binary image is separated from the trachea adhering to the lung parenchyma by mathematical morphology [48] . Then combined with the hole filling algorithm to remove the lung trachea tree and small-area lung parenchyma holes, the strel function expansion corrosion is used to remove the boundary irrelevant to the lung parenchyma in the image and smooth the edge of the lung parenchyma, in order to obtain a complete lung parenchyma template.…”

SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation

“… • The effectiveness of this approach can be further investigated on some standard image datasets. Chakraborty et al [50] Unsupervised approach NA • A morphological recreation activity-based chest CT picture division approach is proposed in this work. • The edge content-based organized network approach is utilized to decide the ideal organizing components.…”