Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment

Abstract

The rapid worldwide spread of the COVID-19 pandemic has infected patients around the world in a short space of time. Chest computed tomography (CT) images of patients who are infected with COVID-19 can offer early diagnosis and efficient forecast monitoring at a low cost. The diagnosis of COVID-19 on CT in an automated way can speed up many tasks and the application of medical treatments. This can help complement reverse transcription-polymerase chain reaction (RT-PCR) diagnosis. The aim of this work is to develop a system that automatically identifies ground-glass opacity (GGO) and pulmonary infiltrates (PIs) on CT images from patients with COVID-19. The purpose is to assess the disease progression during the patient's follow-up assessment and evaluation. We propose an efficient methodology that incorporates oversegmentation mean shift followed by superpixel-SLIC (simple linear iterative clustering) algorithm on CT images with COVID-19 for pulmonary parenchyma segmentation. To identify the pulmonary parenchyma, we described each superpixel cluster according to its position, grey intensity, second-order texture, and spatial-context-saliency features to classify by a tree random forest (TRF). Second, by applying the watershed segmentation to the mean-shift clusters, only pulmonary parenchyma segmentation-identified zones showed GGO and PI based on the description of each watershed cluster of its position, grey intensity, gradient entropy, second-order texture, Euclidean position to the border region of the PI zone, and global saliency features, after using TRF. Our classification results for pulmonary parenchyma identification on CT images with COVID-19 had a precision of over 92% and recall of over 92% on twofold cross validation. For GGO, the PI identification showed 96% precision and 96% recall on twofold cross validation.

Figure 1

Input images and ground truth. (a–f) Computed tomography (CT) in a patient with COVID-19, (g–l) ground-truth data for identification of pulmonary parenchyma, and (m–r) ground-truth data for ground-glass opacity (GGO) and pulmonary infiltrate (PI) identification.

Figure 2

Overall method description.

Figure 3

Image oversegmentation. (a) Input grey-scale image, (b) Mean-shift L band image clusters.

Figure 4

Pulmonary parenchyma identification. (a) Superpixel-SLIC segmentation, (b) result from tree random forests (TRF), (c) and spatial-context-saliency feature extraction and connections.

Figure 5

GGO and PI identification. (a) Mean-shift clusters on the identified pulmonary parenchyma zone, (b) results from TRF, and (c) watershed clusters and border regions (blue dots).

Figure 6

Qualitative identification results of abnormal CT results with COVID-19, including pulmonary parenchyma (blue outline) and GGO and PIs (red outline).