Improving the Quality of X-Ray Images of the Lungs of COVID-19 and Healthy Patients Using the Contrast Limited Adaptive Histogram Equalization (CLAHE) Method in Batam
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Abstract
X-ray imaging is a widely used technique for observing lung patients conditions. Compared to other radiographic methods, X-ray is more accessible, cost-effective, and commonly available in healthcare facilities. However, digital X-ray images often suffer from low quality, particularly in terms of image contrast, which complicates the process of identifying lung abnormalities accurately. In Embung Fatimah Hospital in Batam, X-ray imaging is routinely used to screen COVID-19 and healthy patients. To address the issue of poor image contrast, this study applies the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, aiming to enhance image clarity and support more effective analysis. The research involved 20 lung X-ray images, consisting of 10 from COVID-19 and 10 from healthy patients, retrieved from the hospital’s radiology department system. The images underwent digital processing using Matlab software. The workflow included converting the images to grayscale before applying contrast enhancement with the CLAHE method, using three different distribution types: Uniform, Rayleigh, and Exponential. Following enhancement, Peak Signal to Noise Ratio and Mean Square Error metrics were calculated for each distribution type to evaluate image quality improvement. The result shown that all three CLAHE methods effectively enhanced the visual contrast of the lung images. The average MSE values for COVID-19 images were 26.27, 25.25, and 25.62, while for healthy images they were 28.27, 27.35, and 27.44. Meanwhile, the average PSNR values for COVID-19 images reached 155.63, 196.58, and 180.58, with healthy images scoring 98.27, 122.22, and 118.97. Overall, the process achieved an accuracy of 100%.
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References
W. Guan et al., “Clinical Characteristics of Coronavirus Disease 2019 in China,” New England Journal of Medicine, vol. 382, no. 18, pp. 1708–1720, Apr. 2020, doi: 10.1056/NEJMoa2002032. DOI: https://doi.org/10.1056/NEJMoa2002032
Y.-C. Wu, C.-S. Chen, and Y.-J. Chan, “The outbreak of COVID-19: An overview,” Journal of the Chinese Medical Association, vol. 83, no. 3, pp. 217–220, Mar. 2020, doi: 10.1097/JCMA.0000000000000270. DOI: https://doi.org/10.1097/JCMA.0000000000000270
A. Sharma, S. Tiwari, M. K. Deb, and J. L. Marty, “Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies,” International Journal of Antimicrobial Agents, vol. 56, no. 2, p. 106054, Aug. 2020, doi: 10.1016/j.ijantimicag.2020.106054. DOI: https://doi.org/10.1016/j.ijantimicag.2020.106054
M. M. Rahaman et al., “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: Clinical Applications of Diagnosis and Therapeutics, vol. 28, no. 5, pp. 821–839, Sep. 2020, doi: 10.3233/XST-200715. DOI: https://doi.org/10.3233/XST-200715
X. Jin et al., “Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms,” Gut, vol. 69, no. 6, pp. 1002–1009, Jun. 2020, doi: 10.1136/gutjnl-2020-320926. DOI: https://doi.org/10.1136/gutjnl-2020-320926
A. U. Anka et al., “Coronavirus disease 2019 (COVID‐19): An overview of the immunopathology, serological diagnosis and management,” Scandinavian Journal of Immunology, vol. 93, no. 4, Apr. 2021, doi: 10.1111/sji.12998. DOI: https://doi.org/10.1111/sji.12998
E. J. Hwang et al., “COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system,” PLOS ONE, vol. 16, no. 6, p. e0252440, Jun. 2021, doi: 10.1371/journal.pone.0252440. DOI: https://doi.org/10.1371/journal.pone.0252440
R. Yasin and W. Gouda, “Chest X-ray findings monitoring COVID-19 disease course and severity,” Egyptian Journal of Radiology and Nuclear Medicine, vol. 51, no. 1, pp. 1–18, Dec. 2020, doi: 10.1186/s43055-020-00296-x. DOI: https://doi.org/10.1186/s43055-020-00296-x
N. Stogiannos, D. Fotopoulos, N. Woznitza, and C. Malamateniou, “COVID-19 in the radiology department: What radiographers need to know,” Radiography, vol. 26, no. 3, pp. 254–263, Aug. 2020, doi: 10.1016/j.radi.2020.05.012. DOI: https://doi.org/10.1016/j.radi.2020.05.012
I. M. D. Maysanjaya, “Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 9, no. 2, pp. 190–195, May 2020, doi: 10.22146/jnteti.v9i2.66. DOI: https://doi.org/10.22146/jnteti.v9i2.66
R. S. Aldoury, N. M. G. Al-Saidi, R. W. Ibrahim, and H. Kahtan, “A new X-ray images enhancement method using a class of fractional differential equation,” MethodsX, vol. 11, p. 102264, Dec. 2023, doi: 10.1016/j.mex.2023.102264. DOI: https://doi.org/10.1016/j.mex.2023.102264
W. Zhang, P. Zhuang, H.-H. Sun, G. Li, S. Kwong, and C. Li, “Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement.,” IEEE Transactions on Image Processing, vol. 31, pp. 3997–4010, 2022, doi: 10.1109/TIP.2022.3177129. DOI: https://doi.org/10.1109/TIP.2022.3177129
Z. Huang, Z. Wang, J. Zhang, Q. Li, and Y. Shi, “Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization,” Optik, vol. 226, p. 165877, Jan. 2021, doi: 10.1016/j.ijleo.2020.165877. DOI: https://doi.org/10.1016/j.ijleo.2020.165877
A. Fawzi, A. Achuthan, and B. Belaton, “Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images,” IEEE Access, vol. 9, pp. 164466–164492, 2021, doi: 10.1109/ACCESS.2021.3134170. DOI: https://doi.org/10.1109/ACCESS.2021.3134170
J. C. M. dos Santos, G. A. Carrijo, C. de Fátima dos Santos Cardoso, J. C. Ferreira, P. M. Sousa, and A. C. Patrocínio, “Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter,” Research on Biomedical Engineering, vol. 36, no. 2, pp. 107–119, Jun. 2020, doi: 10.1007/s42600-020-00046-y. DOI: https://doi.org/10.1007/s42600-020-00046-y
G. F. C. Campos, S. M. Mastelini, G. J. Aguiar, R. G. Mantovani, L. F. de Melo, and S. Barbon, “Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 59, pp. 1–18, Dec. 2019, doi: 10.1186/s13640-019-0445-4. DOI: https://doi.org/10.1186/s13640-019-0445-4
S. N. Nia and F. Y. Shih, “Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 12, 2024, doi: 10.1142/S0218001424570106. DOI: https://doi.org/10.1142/S0218001424570106
F. F. Alkhalid, A. M. Hasan, and A. A. Alhamady, “Improving radiographic image contrast using multi layers of histogram equalization technique,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 1, p. 151, Mar. 2021, doi: 10.11591/ijai.v10.i1.pp151-156. DOI: https://doi.org/10.11591/ijai.v10.i1.pp151-156
Marisha Pertiwi, “Identifikasi Citra Paru-Paru pada Pasien COVID-19 dengan Teknik Edge Detection,” Jurnal Sistim Informasi dan Teknologi, Aug. 2022, doi: 10.37034/jsisfotek.v4i4.146. DOI: https://doi.org/10.37034/jsisfotek.v4i4.146
Dodi Andre Putra, J. Na` am, and Yuhandri, “Identifikasi Objek pada Citra Thorax X-Ray Pasien COVID-19 dengan Metode Contrast Limited Adaptive Histogram Equalization (CLAHE),” Jurnal Informasi dan Teknologi, vol. 4, no. 1, pp. 33–38, Feb. 2022, doi: 10.37034/jidt.v4i1.184. DOI: https://doi.org/10.37034/jidt.v4i1.184
Suharyanto, Z. A. Hasibuan, P. N. Andono, D. Pujiono, and R. I. M. Setiadi, “Contrast Limited Adaptive Histogram Equalization for Underwater Image Matching Optimization use SURF,” Journal of Physics: Conference Series, vol. 1803, no. 1, p. 012008, Feb. 2021, doi: 10.1088/1742-6596/1803/1/012008. DOI: https://doi.org/10.1088/1742-6596/1803/1/012008
K. Saputra S, I. Taufik, D. Farahdilla Dharma, and M. Hidayat, “Analisis Perbaikan Kualitas Citra Menggunakan CLAHE dan HE Pada Citra X-Ray Covid-19 dan Pneumonia,” IJCIT (Indonesian Journal on Computer and Information Technology), vol. 6, no. 2, Dec. 2021, doi: 10.31294/ijcit.v6i2.10855. DOI: https://doi.org/10.31294/ijcit.v6i2.10855
A. S. Wilianti and S. Agoes, “Pengolahan Citra untuk Perbaikan Kualitas Citra Sinar-X Dental Menggunakan Metode Filtering,” Jetri : Jurnal Ilmiah Teknik Elektro, vol. 17, no. 1, pp. 31–46, Aug. 2019, doi: 10.25105/jetri.v17i1.4492. DOI: https://doi.org/10.25105/jetri.v17i1.4492
S. S. Sumijan, A. W. Purnama, and S. Arlis, “Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 6, p. 591, Dec. 2019, doi: 10.25126/jtiik.201966870. DOI: https://doi.org/10.25126/jtiik.201966870
R. K. Hapsari, M. I. Utoyo, R. Rulaningtyas, and H. Suprajitno, “Comparison of Histogram Based Image Enhancement Methods on Iris Images,” Journal of Physics: Conference Series, vol. 1569, no. 2, p. 022002, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022002. DOI: https://doi.org/10.1088/1742-6596/1569/2/022002
M. W. Mirza, A. Siddiq, and I. R. Khan, “A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images,” Signal, Image and Video Processing, vol. 17, no. 4, pp. 915–924, Jun. 2023, doi: 10.1007/s11760-022-02214-2. DOI: https://doi.org/10.1007/s11760-022-02214-2
B. Kadhim Oleiwi, L. H. Abood, and M. Issa Al Tameemi, “Human visualization system based intensive contrast improvement of the collected COVID-19 images,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 3, p. 1502, Sep. 2022, doi: 10.11591/ijeecs.v27.i3.pp1502-1508. DOI: https://doi.org/10.11591/ijeecs.v27.i3.pp1502-1508