Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

作者：

Highlights：

• Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as structure.

• Efficient Kapur entropy-based multilevel thresholding unsupervised procedure.

• Measure, visualize, and study comparisons of the infected by COVID-19 volume.

• The proposed reach the desired heat-mapping results of the lesion and has the potential to be used for clinical applications.

摘要

•Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as structure.•Efficient Kapur entropy-based multilevel thresholding unsupervised procedure.•Measure, visualize, and study comparisons of the infected by COVID-19 volume.•The proposed reach the desired heat-mapping results of the lesion and has the potential to be used for clinical applications.

论文关键词：Corona-virus Ddisease (COVID-19),Computer-Aaided Ddetection (CAD),COVID-19 lesion,Segmentation,Color-mapping,3D Visualization

论文评审过程：Received 23 June 2020, Revised 26 September 2020, Accepted 1 November 2020, Available online 2 November 2020, Version of Record 2 March 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107747