Computed Tomography Image Segmentation of Lung Corona Virus Infection Region Based on Combination of Grayscale Morphological Reconstruction and Fast Marching Method

Abstract

Recently, X-ray computed tomography-imaging modality is considered as golden standard for diagnosis of coronavirus lungs infection. In worldwide, infectious patients increase rapidly that lead to weariness in health services staff, as well as instant treatment required to avoid patients’ health deterioration due to infection development. Image processing would be reinforcing health services by considering computer-based segmentation. However, a ground glass computed tomography image fashion of coronavirus lungs infection characterized by disappearance of edge region of interest and lack of object structure. In this study, these challenges addressed by introducing a new algorithm that combined both morphological reconstruction and fast marching method. The proposed algorithm applied on archived computed tomography dataset for coronavirus infected patients, results showed consistent determination of ground glass infection region compared to manual delineation of senior physician. The proposed algorithm restricted to empirical adjustment of FMM’s threshold that would be addressed in upcoming study.