Abstract
This research work is carried out to quantify the COVID-19 disease and to explore whether the quantitative can be used to analyze the survivability of the patient during admission. In this method, a novel percentage split distribution (PSD), thresholding-based image segmentation method is proposed to quantify normal and lesion regions by analyzing the benign GGOs. The method segments the lung-CT image based on pixel distribution. The segmented regions are quantified as a fraction of region of interest with total number of pixels. The study is also extended to analyze the left and right lungs separately with some common findings on lesion distribution involved with COVID-19 disease. The performance of PSD method has been compared with two traditional image segmentation-based methods. From the results, it has been observed that the segments created by the PSD method are better than experimental methods and clearly identify the margins of lesion and normal regions.
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Joy Christy, A., Umamakeswari, A. (2023). Analysis of High-Resolution CT Images of COVID-19 Patients. In: Pandey, S., Shanker, U., Saravanan, V., Ramalingam, R. (eds) Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-15542-0_12
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DOI: https://doi.org/10.1007/978-3-031-15542-0_12
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