Severity Assessment of COVID-19 Based on Feature Extraction and V-Descriptors

Abstract

Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of COVID-19, we propose a new method that identifies rich features of lung infections from a chest CT image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

“…e SVM classifier was used to select four common kernel functions for recognition. e results showed that the SVM method had a good classification effect in dealing with small sample problems [7,8]. A tobacco disease image retrieval method based on the spot feature fusion was proposed to diagnose 7 common tobacco diseases with high recognition accuracy.…”

“…In formula (8), M is the autocorrelation matrix of 2 × 2, which can be calculated by the derivative of the image as shown.…”

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans

“…Finally, they confirmed that radioactive characteristics can be used as an index to distinguish COVID-19 from pneumonia. Ye et al [ 21 ] proposed a method to extract features such as roughness and contrast from chest CT images to confirm the infected area and then extract an outline to detect lesions. Finally, the texture features and V-descriptors were fused to describe the severity of the disease.…”

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

“…As an example, Ye et al. combined the infection morphological features, and the texture attributes like coarseness, contrast, roughness, and entropy, to create a fusion assessment descriptor for severity estimation [16] . Along similar lines, Wu et al.…”