The analysis of differences at Binary Image in COVID-19 and ARDS Patients from chest X-Ray examination

Syntia Anggraeni  -  Department of Physics, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Indonesia
Siska Nuryani  -  Department of Physics, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Indonesia
Heni Sumarti*    -  Department of Physics, Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang, Indonesia
Samuel Gideon  -  Department of Chemical Engineering, Politeknik Teknologi Kimia Industri, Medan, Indonesia

(*) Corresponding Author
DOI: 10.21580/jnsmr.2022.8.1.10890

Corona virus disease 2019 (COVID-19), a viral infection that was discovered at the end of December 2019 in Wuhan, China. The spread and transmission of this virus is very fast even to all countries in the world. Meanwhile, Acute Respiratory Distress Syndrome (ARDS) is an emergency condition in the field of pulmonology that occurs due to fluid accumulation in the alveoli causes gas exchange disorders so that oxygen distribution to tissues were reduced. In this study, Chest X-Ray (CXR) image processing done in COVID-19 and ARDS patients with the aim of analyzing the differences in binary image using the Otsu Thresholding method. This study prioritizes improving the quality of the original CXR image by segmentation using calculating the Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) values. The results showed that the difference between CXR images in COVID-19 patients and ARDS lies in the extent of spread, in COVID-19 patients the extent of spread varies depending on the length of time the virus has invaded and not all of it starts from the alveolus, while ARDS tends to be constant and starts from the lungs. The lower part of the lung, specifically the alveoli. 

©2022 JNSMR UIN Walisongo. All rights reserved.

Keywords: Covid-19; ARDS; Otsu; PSNR; MSE; Binary image

  1. A. Zein, “Pendeteksian Virus Corona dalam Gambar X-Ray Menggunakan Algoritma Artificial Intelligence dengan Deep Learning Python,” Teknol. Inf. ESIT, vol. XV, no. 01, pp. 19–23, 2019.
  2. R. L-L, “Identification of a Novel Corona Virus Causing Severe Pneumonia In Human,” Chin Med. 2020.
  3. W. H. Organization, “Naming The Coronavirus Disease (COVID-19) And The Virus That Causes It.” 2020.
  4. W. H. Organization, “WHO Director- General’s Opening Remark At The Media Briefing On COVID-19.” 2020.
  5. Yuliana, “Corona Virus Diseases (Covid-19),” Wellness Heal. Mag., vol. 2, no. 1, pp. 187–192, 2020.
  6. A. Bakhtiar and R. A. Maranatha, “Acute Respiratory Distress Syndrome,” Respirasi, vol. 4, no. 2, pp. 51–60, 2018.
  7. M. S. Wibawa, “Studi Komparasi Metode Segmentasi Paru-Paru Pada Citra CT-SCAN Aksial,” J. Nas. Pendidik. Tek. Inform., vol. 7, no. 3, pp. 283–292, 2018.
  8. Di. Lestaringsih, “Evaluasi Penataan Mual Muntah Karena Kemoterapi Pada Pasien Kanker Paru-Paru Di Instalasi Rawat Inap RSUD Surakarta.” 2010.
  9. H. Sumarti, “Analisis Perkembangan Pasien Covid-19 Menggunakan Segmentasi Citra Rontgen Toraks,” J. Fis. dan Ter., vol. 7, pp. 15–23, 2020.
  10. V. Rajinikanth, N. Dey, A. N. J. Raj, A. E. Hassanien, K. C. Santosh, and N. S. M. Raja, “Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images,” Appl. Sci., vol. 6, no. April 2020, 2020, [Online]. Available: http://arxiv.org/abs/2004.03431.
  11. S. C. Satapathy, D. J. Hemanth, S. Kadry, G. Manogaran, and N. Hannon, “Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm,” Res. Sq., 2020, doi: 10.21203/rs.3.rs-40148/v1.
  12. M. F. Iqbal, “Deteksi Status Kanker Paru-Paru Pada Citra CT- Scan Menggunakan Metode Fuzzy Logic,” Tek. Elektro, vol. 7, no. 3, pp. 121–126, 2018.
  13. Github, “Data Hasil Citra Rontgen Pasien Covid-19 Dan ARDS.” 2020.
  14. R. T. Wahyuningrum, S. I. Syafi’i, and A. Mumtasa, “Segmentasi Obyek Pada Citra Digital Menggunakan Metode Otsu Thresholding,” J. Inform., vol. 13, no. 1, pp. 1–8, 2015, doi: 10.9744/informatika.13.1.1-8.
  15. P. Harnis, Y. A. Sari, P. Studi, T. Informatika, F. I. Komputer, and U. Brawijaya, “Segmentasi Citra Kue Tradisional menggunakan Otsu Thresholding pada Ruang Warna CIE LAB,” J. Pengemb. Teknol. Inf. dan ilmu Komput., vol. 3, no. 7, pp. 6799–6808, 2019.
  16. H. R. Fajrin, P. Studi, T. Elektromedik, P. Vokasi, and U. Muhammadiyah, “Perbandingan metode untuk perbaikan kualitas citra mammogram,” simetris, vol. 7, no. 2, pp. 657–664, 2016.
  17. K. L. Gould et al., “Anatomic Versus Physiologic Assessment of Coronary Artery Disease Role of Coronary Flow Reserve , Fractional Flow Reserve , and Positron Emission Tomography Imaging in Revascularization,” J. Am. Coll. Cardiol., vol. 62, no. 18, pp. 1639–53, 2013, doi: 10.1016/j.jacc.2013.07.076.
  18. Z. Amin, F. N. Rahmawati, C. O. Mamudi, and A. P. Amanda, “Clinical characteristics of acute respiratory distress syndrome survived patients at a tertiary hospital in Jakarta,” Med. J. Indones., vol. 26, no. 1, pp. 35–39, 2017.
  19. R. Rahmadewi, “Analisa perbandingan beberapa metode deteksi tepi pada citra rontgen penyakit paru-paru,” integrasi, vol. 1, no. 2, pp. 21–29, 2016.
  20. S. A. Ñamendys-Silva, “ECMO for ARDS due to COVID-19,” Hear. Lung, vol. 49, no. 4, pp. 348–349, 2020, doi: 10.1016/j.hrtlng.2020.03.012.
  21. X. Tang et al., “Comparison of Hospitalized Patients With ARDS Caused by COVID-19 and H1N1,” Chest, vol. 158, no. 1, pp. 195–205, 2020, doi: 10.1016/j.chest.2020.03.032.
  22. S. S. Batah and A. T. Fabro, “Pulmonary
  23. pathology of ARDS in COVID-19: A pathological review for clinicians,” Respir. Med., vol. 176, no. November 2020, p. 106239, 2021, doi: 10.1016/j.rmed.2020.106239.
  24. T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., vol. 121, no. April, p. 103792, 2020, doi: 10.1016/j.compbiomed.2020.103792.
  25. A. D. T. Force, “Acute Respiratory Distress Syndrome, The Berlin Definition,” JAMA, vol. 23, no. 307, 2012.
  26. I. Maulana, “Analisis Perbandingan Metode Segmentasi Media Filter Dalam Reduksi Noise Salt Dan Pepper,” Cogito Smart J., vol. 2, no. 2, 2016.
  27. T. Arifin, “Analisis Perbandingan Metode Segmentasi Citra Pada Citra Mammografi,” Informatika, vol. 3, pp. 156–163, 2016.
  28. T. P. Mallini, E. Junianto, and Y. Ramdhani, “Deteksi Diameter Kanker Payudara Menggunakan Segmentasi Citra Berdasarkan Metode Otsu Thresholding,” J. RESPONSIF, vol. 2, no. 2, pp. 204–212, 2020.

Open Access Copyright (c) 2022 Journal of Natural Sciences and Mathematics Research
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Journal of Natural Sciences and Mathematics Research
Published by Faculty of Science and Technology
Universitas Islam Negeri Walisongo Semarang

Jl Prof. Dr. Hamka Kampus III Ngaliyan Semarang 50185
Website: https://journal.walisongo.ac.id/index.php/JNSMR
Email:jnsmr@walisongo.ac.id

ISSN: 2614-6487 (Print)
ISSN: 2460-4453 (Online)

View My Stats

Lisensi Creative Commons

This work is licensed under a Creative Commons Lisensi Creative Commons .

apps