Analisis Kualitas Citra Tegangan 48 kV dan 50 kV Pesawat Sinar-X Pada Pasien Covid-19 dengan Menggunakan Software Image-J
Abstract
The process of analysing the quality of radiographic images produced by X-rays with 48 kV and 50 kV tube voltages in Covid-19 patients using ImageJ software has been carried out. The images analysed are the results of thoracic radiographic examinations using the same electric current strength in Covid-19 patients with 48 kV and 50 kV voltage variations. The image results come from converting the analogue format to digital format for further processing. The analysis method uses the FFT (Fast Fourier Transform) method and histogram analysis. Image quality was assessed based on pixel distribution, contrast and SNR (Signal to Noise Ratio) parameters. Visualisation of the distribution can be seen based on histogram analysis. In the histogram, the prominent intensity shows the pixel intensity and the peak width shows the contrast range in the image. If the histogram graph is distributed in the minimum area, the image tends to be dark and vice versa, if the histogram graph is more in the maximum area, the image tends to be bright. The results show that the 48 kV voltage has a more even distribution but has low contrast and SNR values compared to the 50 kV voltage. The results of this study provide an overview of the selection of kinetic parameters in patients and optimisation of X-ray technical parameters to obtain maximum image quality in radiographic diagnosis.
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References
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[5] G. N. Sutapa, A. Hanzani, W. B. Sudarsana, N. N. Ratini, N. L. P. Trisnawati, and W. T. Baskoro, “Identifikasi Citra Radiografi Sinar-X Pemeriksaan Thorax Untuk Penderita Covid-19 Di Instalasi Radiologi RSUP Prof. I. G. N. G Ngoerah,” Kappa J., vol. 8, no. 2, Art. no. 2, Aug. 2024, doi: 10.29408/kpj.v8i2.25290.
[6] R. Ariza, A. D. Messah, F. Sinaga, A. Wahyudi, S. A. Pratama, and I. Annisa, “Korelasi Gambaran Radiografi Toraks dengan Karakteristik Klinis Pasien Terkonfirmasi Covid-19,” ARTERI J. Ilmu Kesehat., vol. 2, no. 1, Art. no. 1, 2020, doi: 10.37148/arteri.v2i1.140.
[7] D. E. Litmanovich, M. Chung, R. R. Kirkbride, G. Kicska, and J. P. Kanne, “Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language,” J. Thorac. Imaging, vol. 35, no. 6, pp. 354–360, Nov. 2020, doi: 10.1097/RTI.0000000000000541.
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[10] N. Ullah, J. Khan, S. El-Sappagh, N. El-Rashidy, and M. Khan, “A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network,” Diagnostics, vol. 13, no. 1, p. 162, Jan. 2023, doi: 10.3390/diagnostics13010162.
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[12] S. Normawati, J. Jumardin, and S. Harmiati, “Pengaruh Tegangan Tabung Sinar-X (kV) Terhadap Kualitas Citra Radiografi Foto Toraks Pasien Covid-19,” Media Ilm. Kesehat. Indones., vol. 2, no. 3, Art. no. 3, Sep. 2024, doi: 10.58184/miki.v2i3.366.
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[14] S. Zelviani, “Kualitas Citra Pada Direct Digital Radiography Dan Computed Radiography,” Teknosains Media Inf. Sains Dan Teknol., vol. 11, no. 1, Art. no. 1, 2017, doi: 10.24252/teknosains.v11i1.7583.
[15] N. P. E. Wibowo, S. Susilo, and S. Sunarno, “Uji Profisiensi Citra Hasil Eksposi Sistem Radiografi Digital Di Laboratorium Fisika Medik UNNES,” Unnes Phys. J., vol. 5, no. 1, Art. no. 1, 2016.
[16] N. W. M. S. utami, Nyoman, and I. P. E. Juliantara, “Pengaruh Kombinasi Arus Tabung Sinar-X dan Waktu Eksposi Terhadap Contrast to Noise Ratio (CNR) dengan menggunakan Computed Radiography,” Bul. Fis., vol. 23, no. 1, Art. no. 1, 2021, doi: 10.24843/BF.2022.v23.i01.p04.
[17] F. Fitriani, S. Zelviani, and S. Sahara, “Pengaruh Tegangan Tabung (Kv) Pada Pemeriksaan Thorax Terhadap Kualitas Citra Radiografi Di Balai Besar Kesehatan Paru Masyarakat Makassar,” JFT J. Fis. Dan Ter., vol. 7, no. 2, Art. no. 2, 2020, doi: 10.24252/jft.v7i2.18067.
[18] T. Sutikno, K, Firdausy, and E, Prasetyo, “Perangkat Lunak Perbaikan Kualitas Citra Digital Model Rgb Dan Ihs Dengan Operasi Peningkatan Kontras,” Semin. Nas. Apl. Teknol. Inf., 2007.
[19] J. Schindelin, C. T. Rueden, M. C. Hiner, and K. W. Eliceiri, “The Image-J ecosystem: An open platform for biomedical image analysis.,” Mol. Reprod. Dev., vol. 82, no. 7–8, Art. no. 7–8, 2015, doi: https://doi.org/10.1002/mrd.22489.
[20] H. M. Labania, P. Rindayani, Kasman, Abd. Rahman, and S. Ulum, “Analisis Kontras Digital Radiography Dengan Menggunakan ImageJ: (Analysis of Digital Radiography Contrast using ImageJ),” Gravitasi, vol. 20, no. 1, pp. 10–18, Jul. 2021, doi: 10.22487/gravitasi.v20i1.15521.
[21] J. Coolen et al., “Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials,” J. Med. Imaging, vol. 8, no. S1, Jan. 2021, doi: 10.1117/1.JMI.8.S1.013501.
[22] Fitriani, Zelviani, and Sahara, “Pengaruh Tegangan Tabung (kV) Pada Pemeriksaan Thorax Terhadap Kualitas Citra Radiografi Dengan Analisis Aplikasi Image-J,” J. Fis. Dan Ter., vol. 07, no. 2, pp. 139–148, 2020, doi: 10.24252/jft.v7i2.18267.
[23] S. Zelviani, “Kualitas Citra Pada Direct Digital Radiography Dan Computed Radiography,” Teknosains Media Inf. Sains Dan Teknol., vol. 11, no. 1, Art. no. 1, 2017, doi: 10.24252/teknosains.v11i1.7583.
[24] N. Barrie Smith and A. G. Webb, Introduction to medical imaging: physics, engineering and clinical applications, Repr. in Cambridge texts in biomedical engineering. Cambridge: Cambridge University Press, 2012.
[25] Z. Fatima, N. Ali, M. A. Williams, S. Dhar, and M. Maqbool, “X-ray scattering and attenuation cross-sections and coefficients of bone, brain, lung, fat, and soft tissue for applications in dosimetry, cancer detection, and treatment,” Radiat. Phys. Chem., vol. 208, p. 110908, Jul. 2023, doi: 10.1016/j.radphyschem.2023.110908.
[26] N. Koshikawa, Y. Kikuchi, Y. Wakabayashi, T. Kobayashi, and J. Kataoka, “High-contrast Compton camera: Challenges to high-quality and broadband imaging,” Appl. Phys. Lett., vol. 125, no. 17, p. 173701, Oct. 2024, doi: 10.1063/5.0231745.
[27] A. P. Sauter et al., “Correlation of image quality parameters with tube voltage in X-ray dark-field chest radiography: a phantom study,” Sci. Rep., vol. 11, no. 1, p. 14130, Jul. 2021, doi: 10.1038/s41598-021-93716-5.
[28] A. A. Oglat, “Comparison of X-ray films in term of kVp, mA, exposure time and distance using Radiographic Chest Phantom as a radiation quality,” J. Radiat. Res. Appl. Sci., vol. 15, no. 4, p. 100479, Dec. 2022, doi: 10.1016/j.jrras.2022.100479.
[29] S. Herlinda, D. Fitriyani, and M. Marzuki, “Analisis Pengaruh Kuat Arus dan Tegangan Terhadap Kualitas Citra Computed Tomography Scan (CT) Siemens Perspective di RSUP Dr. M. Djamil Padang,” POSITRON, vol. 9, no. 1, p. 39, May 2019, doi: 10.26418/positron.v9i1.31138.
[30] M. Fathunnisa, Pratiwi Sri Wardani, and Devina Rayzy Perwitasari, “Analysis of the Effect of Age on the Absorption Dose of X-ray Radiation in Thorax Examination,” J. Inotera, vol. 8, no. 2, pp. 284–289, Sep. 2023, doi: 10.31572/inotera.Vol8.Iss2.2023.ID255.
Published
2024-12-23
How to Cite
JUMARDIN, Jumardin et al.
Analisis Kualitas Citra Tegangan 48 kV dan 50 kV Pesawat Sinar-X Pada Pasien Covid-19 dengan Menggunakan Software Image-J.
Progressive Physics Journal, [S.l.], v. 5, n. 2, p. 435-447, dec. 2024.
ISSN 2722-7707.
Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/ppj/article/view/1433>. Date accessed: 15 jan. 2025.
doi: https://doi.org/10.30872/ppj.v5i2.1433.
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Articles
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