Analysis of chest X-Ray (CXR) images in COVID-19 patients based on age using the Otsu thresholding segmentation method

Uhty Maesyaroh  -  Universitas Islam Negeri Walisongo Semarang, Indonesia
Laelatul Munawaroh  -  Universitas Islam Negeri Walisongo Semarang, Indonesia
Heni Sumarti*    -  Universitas Islam Negeri Walisongo Semarang, Indonesia
Rico Adrial  -  Universitas Andalas, Indonesia

(*) Corresponding Author
DOI: 10.21580/jnsmr.2021.7.2.10891

The infection with the COVID-19 virus or better known as the Corona virus spread throughout China and other countries around the world until it was designated a pandemic by the World Health Organization (WHO). Detection of patients infected with COVID-19 in the form of RT-PCR, CT-Scan images and Chest X-Ray (CXR). This study aims to analyze CXR images of COVID-19 patients based on age using Otsu Thresholding Segmentation. The image segmentation process uses the Otsu auto-tresholding method to separate objects from the background on the CXR image. The results show that the images of COVID-19 patients have pneumonia spots that are not visible on the original CXR image. The average value of the accuracy of the Otsu Thresholding results is 95.18%. Penunomia spots are mostly found in COVID-19 patients aged 50 to 70 years and over which cause severe lung damage.

©2021 JNSMR UIN Walisongo. All rights reserved.

Keywords: COVID-19; segmentation; Otsu thresholding; CXR; age

  1. W. Swastika, P. Studi, T. Informatika, and P. Korespondensi, “Studi Awal Deteksi Covid-19 Menggunakan Citra CT Berbasis Deep Preliminary Study Of Covid-19 Detection Using CT Image Based On,” vol. 7, no. 3, pp. 629–634, 2020, doi: 10.25126/jtiik.202073399.
  2. Satuan Tugas COVID-19, “Peta Sebaran Covid-19,” Gugus Tugas Percepatan Penanganan Covid-19, 2021. https://covid19.go.id/peta-sebaran.
  3. S. Richardson et al., “Presenting Characteristics, Comorbidities, and Outcomes among 5700 Patients Hospitalized with COVID-19 in the New York City Area,” JAMA - J. Am. Med. Assoc., vol. 323, no. 20, pp. 2052–2059, 2020, doi: 10.1001/jama.2020.6775.
  4. K. C. Liu et al., “CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity,” Eur. J. Radiol., vol. 126, no. February, p. 108941, 2020, doi: 10.1016/j.ejrad.2020.108941.
  5. D. Wang et al., “Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China,” JAMA - J. Am. Med. Assoc., vol. 323, no. 11, pp. 1061–1069, 2020, doi: 10.1001/jama.2020.1585.
  6. Z. P. Ali Narin, Ceren Kaya, “Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks.”
  7. J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, “COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection,” 2020.
  8. M. Hosseiny, S. Kooraki, A. Gholamrezanezhad, S. Reddy, and L. Myers, “Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome,” no. May, pp. 1078–1082, 2020.
  9. H. Yuen Frank Wong et al., “Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients Authors,” Radiology, vol. xxx, p. xxx, 2020.
  10. Y. S. Hariyani, S. Hadiyoso, and T. S. Siadari, “Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 8, no. 2, p. 443, 2020, doi: 10.26760/elkomika.v8i2.443.
  11. M. Ghozali and H. Sumarti, “Deteksi Tepi pada Citra Rontgen Penyakit COVID-19 Menggunakan Metode Sobel,” J. Imejing Diagnostik, vol. 6, pp. 51–59, 2020.
  12. M. S. Wibawa and I. M. A. W. Putra, “Studi Komparasi Metode Segmentasi Paru-Paru Pada Citra CT-Scan Aksial,” vol. 7, pp. 283–292, 2018.
  13. 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.
  14. Y. Liu et al., “Association between age and clinical characteristics and outcomes of COVID-19,” Eur. Respir. J., vol. 318, no. 6, 2020, doi: 10.1183/13993003.01112-2020.
  15. J. Paul Cohen, “Open database of COVID-19 cases with chest X-ray or CT images,” 2020. [Online]. Available: https://github.com/ieee8023/covid-chestxray-dataset.
  16. D. R. Anamisa, “Rancang Bangun Metode OTSU Untuk Deteksi Hemoglobin,” S@Cies, vol. 5, no. 2, pp. 106–110, 2015, doi: 10.31598/sacies.v5i2.64.
  17. D. Abdullah, E. D. Putra, and J. Pseudocode, “Segmentasi Pada Citra Digital Metode Fuzzy C-Means Dan Otsu,” pp. 72–80, 2017.
  18. R. T. Wahyuningrum, “Segmentasi Obyek Pada Citra Digital Menggunakan,” vol. 13, no. 1, pp. 1–8, 2015, doi: 10.9744/informatika.13.1.1-8.
  19. D. Putra, “Binerisasi citra tangan dengan metode otsu,” vol. 3, no. 2, pp. 11–13, 2004.
  20. T. Arifin, “Analisa Perbandingan Metode Segmentasi Citra Pada Citra Mammogram,” J. Inform., vol. 3, no. 2, pp. 156–163, 2016.
  21. R. Kosasih, “Pendeteksian tumor otak dengan menggunakan metode segmentasi otsu,” no. August 2017, 2019.
  22. M. I. Farih, L. Hakim, and M. Munir, “Segmentasi Citra Wayang Dengan Metode Otsu,” vol. 11, no. 01, pp. 8–18, 2016.
  23. A. Borghesi et al., “Radiographic severity index in COVID-19 pneumonia: relationship to age and sex in 783 Italian patients,” Radiol. Medica, vol. 125, no. 5, pp. 461–464, 2020, doi: 10.1007/s11547-020-01202-1.
  24. R. Hossain et al., “CT scans obtained for nonpulmonary indications: Associated respiratory findings of COVID-19,” Radiology, vol. 296, no. 3, pp. E173–E179, 2020, doi: 10.1148/radiol.2020201743.
  25. T. H. Siagian, “Corona Dengan Discourse Network Analysis,” J. Kebijak. Kesehat. Indones., vol. 09, no. 02, pp. 98–106, 2020.
  26. 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.
  27. C. Shen et al., “Comparative Analysis of Early-Stage Clinical Features Between COVID-19 and Influenza A H1N1 Virus Pneumonia,” Front. Public Heal., vol. 8, no. May, pp. 1–7, 2020, doi: 10.3389/fpubh.2020.00206.
  28. A. R. Sahin, “2019 Novel Coronavirus (COVID-19) Outbreak: A Review of the Current Literature,” Eurasian J. Med. Oncol., vol. 4, no. 1, pp. 1–7, 2020, doi: 10.14744/ejmo.2020.12220.
  29. X. Chen et al., “Differences between COVID-19 and suspected then confirmed SARS-CoV-2-negative pneumonia: A retrospective study from a single center,” J. Med. Virol., vol. 92, no. 9, pp. 1572–1579, 2020, doi: 10.1002/jmv.25810.

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