Identifikasi Objek pada Citra Thorax X-Ray Pasien COVID-19 dengan Metode Contrast Limited Adaptive Histogram Equalization (CLAHE)
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Abstract
Chest X-Ray radiography produces digital radiographic images of the chest area such as the lungs, heart, and ribs. This image can visualize the lung condition of COVID-19 patients. Examination of the lung condition of COVID-19 patients with X-Ray is easier, cheaper, and widely available in hospitals than other radiographic techniques. However, the results of the X-Ray radiography digital image have poor quality, so they need to be improved. Low image contrast is a factor in the difficulty of identifying thorax images of COVID-19 patients. Increase the contrast of the low thorax image of COVID-19 patients with Contrast Limited Adaptive Histogram Equalization (CLAHE) so that it is easier to observe concretely and more clearly. The images that were processed in this study were 100 thorax images of COVID-19 patients sourced from the radiology department of Bhayangkara Hospital, Padang Indonesia. Furthermore, the image is processed using digital image processing using Matlab software. The processing stages of the thorax image are converted into grayscale form. The resulting grayscale image is continued with contrast processing using the CLAHE method with Uniform, Rayleigh and Exponential distribution types. The calculation of the Peak Signal to Noise Ratio (PNSR) and Mean Square Error (MSE) values of the image results from the processing of each type of CLAHE was continued. The results of testing all images can be visually improved in contrast quality. The average MSE CLAHE Uniform, Rayleigh and Exponential results were 27.68, 25.86 and 26.33, respectively. The average values of CLAHE Uniform, Rayleigh and Exponential PNSR > 30 dB are 112.32 dB, 171.95 dB and 151.90 dB, which means the CLAHE image is similar to the original image. CLAHE Rayleigh gives the best results in terms of quality and quantity with a total of 85 images or an accuracy value of 85%, while CLAHE Exponential and CLAHE Uniform are 15% and 0%, respectively.
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Dodi Andre Putra, Na` amJ., & Yuhandri. (2022). Identifikasi Objek pada Citra Thorax X-Ray Pasien COVID-19 dengan Metode Contrast Limited Adaptive Histogram Equalization (CLAHE). Jurnal Informasi Dan Teknologi, 4(1), 33-38. https://doi.org/10.37034/jidt.v4i1.184
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[19] Wilianti, A. S., & Agoes, S. (2019). Pengolahan Citra untuk Perbaikan Kualitas Citra Sinar-X Dental Menggunakan Metode Filtering. Jetri : Jurnal Ilmiah Teknik Elektro, 17(1), 31. https://doi.org/10.25105/jetri.v17i1.4492
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[23] Setiadi, D. R. (2020). PSNR vs SSIM: Imperceptibility Quality Assessment for Image Steganography. Multimedia Tools and Applications, 80(6), 8423–8444. https://doi.org/10.1007/s11042-020-10035-z
[2] Wu, Y.-C., Chen, C.-S., & Chan, Y.-J. (2020). The Outbreak of COVID-19: An Overview. Journal of the Chinese Medical Association, 83(3), 217–220. https://doi.org/10.1097/jcma.0000000000000270
[3] Sharma, A., Tiwari, S., Deb, M. K., & Marty, J. L. (2020). Severe Acute Respiratory Syndrome Coronavirus-2 (Sars-Cov-2): A Global Pandemic and Treatment Strategies. International Journal of Antimicrobial Agents, 56(2), 106054. https://doi.org/10.1016/j.ijantimicag.2020.106054
[4] Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X. (2020). Identification Of Covid-19 Samples From Chest X-Ray Images Using Deep Learning: A Comparison Of Transfer Learning Approaches. Journal of X-Ray Science and Technology, 28(5), 821–839. https://doi.org/10.3233/xst-200715
[5] Singhal, T. (2020). A Review of Coronavirus Disease 2019 (COVID-19). The Indian Journal of Pediatrics, 87(4), 281–286. https://doi.org/10.1007/s12098-020-03263-6
[6] Anka, A. U., Tahir, M. I., Abubakar, S. D., Alsabbagh, M., Zian, Z., Hamedifar, H., Sabzevari, A., & Azizi, G. (2020). Coronavirus Disease 2019 (COVID‐19): An Overview of The Immunopathology, Serological Diagnosis and Management. Scandinavian Journal of Immunology, 93(4). https://doi.org/10.1111/sji.12998 99
[7] Hwang, E. J., Kim, K. B., Kim, J. Y., Lim, J.-K., Nam, J. G., Choi, H., Kim, H., Yoon, S. H., Goo, J. M., & Park, C. M. (2021). COVID-19 Pneumonia on Chest X-Rays: Performance of A Deep Learning-Based Computer-Aided Detection System. Plos One, 16(6). https://doi.org/10.1371/journal.pone.0252440 8
[8] Yasin, R., & Gouda, W. (2020). Chest X-Ray Findings Monitoring Covid-19 Disease Course and Severity. Egyptian Journal of Radiology and Nuclear Medicine, 51(1). https://doi.org/10.1186/s43055-020-00296-x
[9] Stogiannos, N., Fotopoulos, D., Woznitza, N., & Malamateniou, C. (2020). Covid-19 in The Radiology Department: What Radiographers Need to Know. Radiography, 26(3), 254–263. https://doi.org/10.1016/j.radi.2020.05.012
[10] Maysanjaya, I. M. (2020). Klasifikasi Pneumonia pada Citra X-Rays Paru-Paru dengan Convolutional Neural Network. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 190–195. https://doi.org/10.22146/jnteti.v9i2.66
[11] dos Santos, J. C., Carrijo, G. A., de Fátima dos Santos Cardoso, C., Ferreira, J. C., Sousa, P. M., & Patrocínio, A. C. (2020). Fundus Image Quality Enhancement for Blood Vessel Detection Via A Neural Network Using CLAHE and Wiener Filter. Research on Biomedical Engineering, 36(2), 107–119. https://doi.org/10.1007/s42600-020-00046-y
[12] Campos, G. F., Mastelini, S. M., Aguiar, G. J., Mantovani, R. G., Melo, L. F., & Barbon, S. (2019). Machine Learning Hyperparameter Selection for Contrast Limited Adaptive Histogram Equalization. EURASIP Journal on Image and Video Processing, 2019(1). https://doi.org/10.1186/s13640-019-0445-4
[13] F. Alkhalid, F., Hasan, A. M., & Alhamady, A. A. (2021). Improving Radiographic Image Contrast Using Multi Layers of Histogram Equalization Technique. IAES International Journal of Artificial Intelligence (IJ-AI), 10(1), 151. https://doi.org/10.11591/ijai.v10.i1.pp151-156
[14] Mat Radzi, S. F., Abdul Karim, M. K., Saripan, M. I., Abd Rahman, M. A., Osman, N. H., Dalah, E. Z., & Mohd Noor, N. (2020). Impact of Image Contrast Enhancement on Stability of Radiomics Feature Quantification on A 2D Mammogram Radiograph. IEEE Access, 8, 127720–127731. https://doi.org/10.1109/access.2020.3008927
[15] Aboshosha, S., Zahran, O., Dessouky, M. I., & Abd El-Samie, F. E. (2019). Resolution and Quality Enhancement of Images Using Interpolation and Contrast Limited Adaptive Histogram Equalization. Multimedia Tools and Applications, 78(13), 18751–18786. https://doi.org/10.1007/s11042-018-7022-1
[16] Lawton, S., & Viriri, S. (2021). Detection of COVID-19 From CT Lung Scans Using Transfer Learning. Computational Intelligence and Neuroscience, 2021, 1–14. https://doi.org/10.1155/2021/5527923
[17] Suharyanto, Hasibuan, Z. A., Andono, P. N., Pujiono, D., & Setiadi, R. I. (2021). Contrast Limited Adaptive Histogram Equalization for Underwater Image Matching Optimization Use Surf. Journal of Physics: Conference Series, 1803(1), 012008. https://doi.org/10.1088/1742-6596/1803/1/012008
[18] S, K. S., Taufk, I., Dharma, D. F., & Hidayat, M. (2021). Analisis Perbaikan Kualitas Citra Menggunakan CLAHE dan HE Pada Citra X-Ray Covid-19 dan Pneumonia. IJCIT (Indonesian Journal on Computer and Information Technology), 6(2), 97–104. https://doi.org/10.31294/ijcit.v6i2.10855
[19] Wilianti, A. S., & Agoes, S. (2019). Pengolahan Citra untuk Perbaikan Kualitas Citra Sinar-X Dental Menggunakan Metode Filtering. Jetri : Jurnal Ilmiah Teknik Elektro, 17(1), 31. https://doi.org/10.25105/jetri.v17i1.4492
[20] Sumijan, S. S., Purnama, A. W., & Arlis, S. (2019). Peningkatan Kualitas Citra CT-Scan Dengan Penggabungan Metode Filter Gaussian dan Filter Median. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(6), 591. https://doi.org/10.25126/jtiik.201966870
[21] Hapsari, R. K., Utoyo, M. I., Rulaningtyas, R., & Suprajitno, H. (2020). Comparison of Histogram Based Image Enhancement Methods on Iris Images. Journal of Physics: Conference Series, 1569, 022002. https://doi.org/10.1088/1742-6596/1569/2/022002
[22] Setiawan, I., Dewanta, W., Nugroho, H. A., & Supriyono, H. (2019). Pengolah Citra Dengan Metode Thresholding Dengan Matlab R2014a. Jurnal Media Infotama, 15(2). https://doi.org/10.37676/jmi.v15i2.868
[23] Setiadi, D. R. (2020). PSNR vs SSIM: Imperceptibility Quality Assessment for Image Steganography. Multimedia Tools and Applications, 80(6), 8423–8444. https://doi.org/10.1007/s11042-020-10035-z