Automated Segmentation of Infected Regions in Chest CT Images of COVID-19 Patients using Supervised Naïve Gaussian Bayes Classifier

Authors

  • Iyad Hatem Tishreen University

Keywords:

Medical image analysis, Image segmentation, COVID-19, CT imaging, Naïve Bayes classifier.

Abstract

In this paper, one hundred chest Computed Tomography images of COVID-19 patients were used to build and test Naïve Gaussian Bayes classifier for discriminating normal from abnormal tissues. Infected areas in these images were manually segmented by an expert radiologist.  Pixel grey value, local entropy and Histograms of Oriented Gradients HOG were extracted as features for tissue image classification. Based on five-folds classification experiments, the accuracy score of the classifier in this fold reached around 79.94%. Classification was more precise (85%) in recognizing normal tissue than abnormal tissue (63%). The effectiveness in identifying positive labels was also more evident with normal tissue than the abnormal one.

Author Biography

Iyad Hatem, Tishreen University

Associate Professor- Faculty of Mechanical and Electrical Engineering

Downloads

Published

2020-12-02

How to Cite

1.
حاتم ا. Automated Segmentation of Infected Regions in Chest CT Images of COVID-19 Patients using Supervised Naïve Gaussian Bayes Classifier. Tuj-eng [Internet]. 2020Dec.2 [cited 2025Jan.22];42(5). Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/10071

Issue

Vol. 42 No. 5 (2020): Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

Section

Articles

License

Copyright (c) 2020 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.