An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images. Issue 1 (23rd November 2020)

• Record Type: Journal Article
• Authors: Selvaraj, Deepika; Venkatesan, Arunachalam; Mahesh, Vijayalakshmi G. V.; Joseph Raj, Alex Noel
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 1(2021)
• Page Start: 28
• Page End: 46
• Publication Date: 2020-11-23
• Subjects: artificial intelligence -- computed tomography image -- deep neural network -- feature extraction -- limited training points -- segmentation -- Zernike moment; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22525
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EMφ ), and structure measure ( S m ) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets.