Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography

Abstract

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19.