Tuberculosis Extra Pulmonary Bacilli Detection System Based on Ziehl Neelsen Images with Segmentation
Keywords:
Bacilli Detection, Image Segmentation, Tuberculosis Extra Pulmonary
Abstract
Tuberculosis Extra Pulmonary (TBEP) is one of the infectious diseases that can cause death. The bacterium Mycobacterium tuberculosis is the cause of this disease. Patients suffering from this disease must be treated quickly. Currently, patients need a long time and a large cost in detecting the bacteria that cause this disease. The technique used is to take the patient's lung fluid by biopsy and given Ziehl Neelsen chemical dye and then observed using a microscope. This study aims to help detect bacteria quickly and precisely by processing the image produced by the microscope. The technique used is to develop the segmentation method. The segmentation process carried out is to develop a Hue Saturation Value (HSV) color space transformation technique with Active Contour, Edge Detection, and Otsu techniques. The images used in this research are 51 images taken from H. Adam Malik Hospital, Medan and have been validated by an expert. Of the several segmentation methods used in this study, the maximum or best result in detecting Tuberculosis Extra Pulmonary (TBEP) bacilli is the Otsu method. So the method developed is very helpful in accelerating the detection of TBEP.
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References
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[8] I. Kurniastuti, T. D. Wulan, and A. Andini, “Color Feature Extraction of Fingernail Image based on HSV Color Space as Early Detection Risk of Diabetes Mellitus,” in 2021 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Oct. 2021, no. October, pp. 51–55, doi: 10.1109/ICOMITEE53461.2021.9650161.
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[14] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Segmentation for Tuberculosis Ziehl-Neelsen Stained Tissue Slide Image Using Thresholding,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019, vol. Agustus, pp. 8–10, doi: 10.1109/CITSM.2018.8674335.
[15] D. R. Silva et al., “Post-tuberculosis lung disease: a comparison of Brazilian, Italian, and Mexican cohorts,” Jornal Brasileiro de Pneumologia, vol. 48, no. 2, pp. 6–11, 2022, doi: 10.36416/1806-3756/E20210515.
[16] F. F. Suharto et al., “Jurnal RSMH Palembang,” Jurnal RSMH Palembang, vol. 1, no. 1, pp. 31–40, 2020.
[17] R. Ullah, S. Khan, Z. Ali, H. Ali, A. Ahmad, and I. Ahmed, “Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data,” Photodiagnosis and Photodynamic Therapy, vol. 39, no. January, p. 102924, 2022, doi: 10.1016/j.pdpdt.2022.102924.
[18] S. A. Wulandari, D. A. Mayasari, M. D. Kurniatie, and R. Tjahyono, “Simplification of Mycobacterium Tuberculosis Segmenting Algorithm in Sputum Images Based of Auto-Thresholding,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, pp. 12–16, doi: 10.1109/iSemantic52711.2021.9573219.
[1] F. J. Dos Santos Reist et al., “BacillusNet: An automated approach using RetinaNet for segmentation of pulmonary Tuberculosis bacillus,” in Proceedings - IEEE Symposium on Computers and Communications, 2021, vol. 2021-Septe, pp. 8–11, doi: 10.1109/ISCC53001.2021.9631390.
[2] T. Ferkol and D. Schraufnagel, “The global burden of respiratory disease,” Annals of the American Thoracic Society, vol. 11, no. 3, pp. 404–406, 2014, doi: 10.1513/AnnalsATS.201311-405PS.
[3] J. Garcia and R. South-Bodiford, “Hematology,” in Small Animal Internal Medicine for Veterinary Technicians and Nurses, J. Garcia and R. South-Bodiford, Eds. Chichester, UK: John Wiley & Sons, Ltd, 2017, pp. 161–192.
[4] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, Dec. 2021, doi: 10.1002/IMA.22556.
[5] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, 2021, doi: 10.1002/ima.22556.
[6] S. Gite, A. Mishra, and K. Kotecha, “Enhanced lung image segmentation using deep learning,” Neural Computing and Applications, vol. 8, no. January, pp. 1–15, Jan. 2022, doi: 10.1007/s00521-021-06719-8.
[7] H. Hartatik, “Diagnosa Penyakit Pulmonary Tuberculosis Dan Extrapulmonary Tuberculosis Menggunakan Algoritma Certainty Factor (CF),” CSRID (Computer Science Research and Its Development Journal), vol. 8, no. 1, pp. 11–24, Mar. 2016, doi: 10.22303/CSRID.8.1.2016.11-24.
[8] I. Kurniastuti, T. D. Wulan, and A. Andini, “Color Feature Extraction of Fingernail Image based on HSV Color Space as Early Detection Risk of Diabetes Mellitus,” in 2021 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Oct. 2021, no. October, pp. 51–55, doi: 10.1109/ICOMITEE53461.2021.9650161.
[9] G. B. Migliori et al., “Tuberculosis and COVID-19 co-infection: description of the global cohort,” European Respiratory Journal, vol. 59, no. 3, pp. 1–15, 2022, doi: 10.1183/13993003.02538-2021.
[10] E. Mique and A. Malicdem, “Deep Residual U-Net Based Lung Image Segmentation for Lung Disease Detection,” in IOP Conference Series: Materials Science and Engineering, Apr. 2020, vol. 803, no. 1, pp. 1–8, doi: 10.1088/1757-899X/803/1/012004.
[11] A. Rachmad, N. Chamidah, and R. Rulaningtyas, “Mycobacterium tuberculosis identification based on colour feature extraction using expert system,” Annals of Biology, vol. 36, no. 2, pp. 196–202, 2020.
[12] T. Rahman et al., “Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization,” in IEEE Access, 2020, vol. 8, pp. 191586–191601, doi: 10.1109/ACCESS.2020.3031384.
[13] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Automated segmentation procedure for Ziehl-Neelsen stained tissue slide images,” in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Aug. 2017, pp. 1–5, doi: 10.1109/CITSM.2017.8089292.
[14] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Segmentation for Tuberculosis Ziehl-Neelsen Stained Tissue Slide Image Using Thresholding,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019, vol. Agustus, pp. 8–10, doi: 10.1109/CITSM.2018.8674335.
[15] D. R. Silva et al., “Post-tuberculosis lung disease: a comparison of Brazilian, Italian, and Mexican cohorts,” Jornal Brasileiro de Pneumologia, vol. 48, no. 2, pp. 6–11, 2022, doi: 10.36416/1806-3756/E20210515.
[16] F. F. Suharto et al., “Jurnal RSMH Palembang,” Jurnal RSMH Palembang, vol. 1, no. 1, pp. 31–40, 2020.
[17] R. Ullah, S. Khan, Z. Ali, H. Ali, A. Ahmad, and I. Ahmed, “Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data,” Photodiagnosis and Photodynamic Therapy, vol. 39, no. January, p. 102924, 2022, doi: 10.1016/j.pdpdt.2022.102924.
[18] S. A. Wulandari, D. A. Mayasari, M. D. Kurniatie, and R. Tjahyono, “Simplification of Mycobacterium Tuberculosis Segmenting Algorithm in Sputum Images Based of Auto-Thresholding,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, pp. 12–16, doi: 10.1109/iSemantic52711.2021.9573219.
[2] T. Ferkol and D. Schraufnagel, “The global burden of respiratory disease,” Annals of the American Thoracic Society, vol. 11, no. 3, pp. 404–406, 2014, doi: 10.1513/AnnalsATS.201311-405PS.
[3] J. Garcia and R. South-Bodiford, “Hematology,” in Small Animal Internal Medicine for Veterinary Technicians and Nurses, J. Garcia and R. South-Bodiford, Eds. Chichester, UK: John Wiley & Sons, Ltd, 2017, pp. 161–192.
[4] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, Dec. 2021, doi: 10.1002/IMA.22556.
[5] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, 2021, doi: 10.1002/ima.22556.
[6] S. Gite, A. Mishra, and K. Kotecha, “Enhanced lung image segmentation using deep learning,” Neural Computing and Applications, vol. 8, no. January, pp. 1–15, Jan. 2022, doi: 10.1007/s00521-021-06719-8.
[7] H. Hartatik, “Diagnosa Penyakit Pulmonary Tuberculosis Dan Extrapulmonary Tuberculosis Menggunakan Algoritma Certainty Factor (CF),” CSRID (Computer Science Research and Its Development Journal), vol. 8, no. 1, pp. 11–24, Mar. 2016, doi: 10.22303/CSRID.8.1.2016.11-24.
[8] I. Kurniastuti, T. D. Wulan, and A. Andini, “Color Feature Extraction of Fingernail Image based on HSV Color Space as Early Detection Risk of Diabetes Mellitus,” in 2021 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Oct. 2021, no. October, pp. 51–55, doi: 10.1109/ICOMITEE53461.2021.9650161.
[9] G. B. Migliori et al., “Tuberculosis and COVID-19 co-infection: description of the global cohort,” European Respiratory Journal, vol. 59, no. 3, pp. 1–15, 2022, doi: 10.1183/13993003.02538-2021.
[10] E. Mique and A. Malicdem, “Deep Residual U-Net Based Lung Image Segmentation for Lung Disease Detection,” in IOP Conference Series: Materials Science and Engineering, Apr. 2020, vol. 803, no. 1, pp. 1–8, doi: 10.1088/1757-899X/803/1/012004.
[11] A. Rachmad, N. Chamidah, and R. Rulaningtyas, “Mycobacterium tuberculosis identification based on colour feature extraction using expert system,” Annals of Biology, vol. 36, no. 2, pp. 196–202, 2020.
[12] T. Rahman et al., “Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization,” in IEEE Access, 2020, vol. 8, pp. 191586–191601, doi: 10.1109/ACCESS.2020.3031384.
[13] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Automated segmentation procedure for Ziehl-Neelsen stained tissue slide images,” in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Aug. 2017, pp. 1–5, doi: 10.1109/CITSM.2017.8089292.
[14] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Segmentation for Tuberculosis Ziehl-Neelsen Stained Tissue Slide Image Using Thresholding,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019, vol. Agustus, pp. 8–10, doi: 10.1109/CITSM.2018.8674335.
[15] D. R. Silva et al., “Post-tuberculosis lung disease: a comparison of Brazilian, Italian, and Mexican cohorts,” Jornal Brasileiro de Pneumologia, vol. 48, no. 2, pp. 6–11, 2022, doi: 10.36416/1806-3756/E20210515.
[16] F. F. Suharto et al., “Jurnal RSMH Palembang,” Jurnal RSMH Palembang, vol. 1, no. 1, pp. 31–40, 2020.
[17] R. Ullah, S. Khan, Z. Ali, H. Ali, A. Ahmad, and I. Ahmed, “Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data,” Photodiagnosis and Photodynamic Therapy, vol. 39, no. January, p. 102924, 2022, doi: 10.1016/j.pdpdt.2022.102924.
[18] S. A. Wulandari, D. A. Mayasari, M. D. Kurniatie, and R. Tjahyono, “Simplification of Mycobacterium Tuberculosis Segmenting Algorithm in Sputum Images Based of Auto-Thresholding,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, pp. 12–16, doi: 10.1109/iSemantic52711.2021.9573219.
[1] F. J. Dos Santos Reist et al., “BacillusNet: An automated approach using RetinaNet for segmentation of pulmonary Tuberculosis bacillus,” in Proceedings - IEEE Symposium on Computers and Communications, 2021, vol. 2021-Septe, pp. 8–11, doi: 10.1109/ISCC53001.2021.9631390.
[2] T. Ferkol and D. Schraufnagel, “The global burden of respiratory disease,” Annals of the American Thoracic Society, vol. 11, no. 3, pp. 404–406, 2014, doi: 10.1513/AnnalsATS.201311-405PS.
[3] J. Garcia and R. South-Bodiford, “Hematology,” in Small Animal Internal Medicine for Veterinary Technicians and Nurses, J. Garcia and R. South-Bodiford, Eds. Chichester, UK: John Wiley & Sons, Ltd, 2017, pp. 161–192.
[4] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, Dec. 2021, doi: 10.1002/IMA.22556.
[5] P. Geetha Pavani, B. Biswal, M. V. S. Sairam, and N. Bala Subrahmanyam, “A semantic contour based segmentation of lungs from chest x-rays for the classification of tuberculosis using Naïve Bayes classifier,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2189–2203, 2021, doi: 10.1002/ima.22556.
[6] S. Gite, A. Mishra, and K. Kotecha, “Enhanced lung image segmentation using deep learning,” Neural Computing and Applications, vol. 8, no. January, pp. 1–15, Jan. 2022, doi: 10.1007/s00521-021-06719-8.
[7] H. Hartatik, “Diagnosa Penyakit Pulmonary Tuberculosis Dan Extrapulmonary Tuberculosis Menggunakan Algoritma Certainty Factor (CF),” CSRID (Computer Science Research and Its Development Journal), vol. 8, no. 1, pp. 11–24, Mar. 2016, doi: 10.22303/CSRID.8.1.2016.11-24.
[8] I. Kurniastuti, T. D. Wulan, and A. Andini, “Color Feature Extraction of Fingernail Image based on HSV Color Space as Early Detection Risk of Diabetes Mellitus,” in 2021 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Oct. 2021, no. October, pp. 51–55, doi: 10.1109/ICOMITEE53461.2021.9650161.
[9] G. B. Migliori et al., “Tuberculosis and COVID-19 co-infection: description of the global cohort,” European Respiratory Journal, vol. 59, no. 3, pp. 1–15, 2022, doi: 10.1183/13993003.02538-2021.
[10] E. Mique and A. Malicdem, “Deep Residual U-Net Based Lung Image Segmentation for Lung Disease Detection,” in IOP Conference Series: Materials Science and Engineering, Apr. 2020, vol. 803, no. 1, pp. 1–8, doi: 10.1088/1757-899X/803/1/012004.
[11] A. Rachmad, N. Chamidah, and R. Rulaningtyas, “Mycobacterium tuberculosis identification based on colour feature extraction using expert system,” Annals of Biology, vol. 36, no. 2, pp. 196–202, 2020.
[12] T. Rahman et al., “Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization,” in IEEE Access, 2020, vol. 8, pp. 191586–191601, doi: 10.1109/ACCESS.2020.3031384.
[13] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Automated segmentation procedure for Ziehl-Neelsen stained tissue slide images,” in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Aug. 2017, pp. 1–5, doi: 10.1109/CITSM.2017.8089292.
[14] B. S. Riza, M. Y. Mashor, M. K. Osman, and H. Jaafar, “Segmentation for Tuberculosis Ziehl-Neelsen Stained Tissue Slide Image Using Thresholding,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019, vol. Agustus, pp. 8–10, doi: 10.1109/CITSM.2018.8674335.
[15] D. R. Silva et al., “Post-tuberculosis lung disease: a comparison of Brazilian, Italian, and Mexican cohorts,” Jornal Brasileiro de Pneumologia, vol. 48, no. 2, pp. 6–11, 2022, doi: 10.36416/1806-3756/E20210515.
[16] F. F. Suharto et al., “Jurnal RSMH Palembang,” Jurnal RSMH Palembang, vol. 1, no. 1, pp. 31–40, 2020.
[17] R. Ullah, S. Khan, Z. Ali, H. Ali, A. Ahmad, and I. Ahmed, “Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data,” Photodiagnosis and Photodynamic Therapy, vol. 39, no. January, p. 102924, 2022, doi: 10.1016/j.pdpdt.2022.102924.
[18] S. A. Wulandari, D. A. Mayasari, M. D. Kurniatie, and R. Tjahyono, “Simplification of Mycobacterium Tuberculosis Segmenting Algorithm in Sputum Images Based of Auto-Thresholding,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, pp. 12–16, doi: 10.1109/iSemantic52711.2021.9573219.
Published
2022-11-22
How to Cite
Riza, B., Na’am, J., & Sumijan, S. (2022). Tuberculosis Extra Pulmonary Bacilli Detection System Based on Ziehl Neelsen Images with Segmentation. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(1), 139-148. https://doi.org/https://doi.org/10.30812/matrik.v22i1.1939
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