An Analysis of COVID-19 using X-ray Image Segmentation based Graph Cut and Box Counting Fractal Dimension

Faiz Ainur Razi

Abstract


COVID-19 is a disease that spreads relatively quickly. So that many victims are infected by this virus. There are various ways to diagnose the body's infection with the coronavirus. One of them with X-ray results. Detecting COVID-19 with the help of an X-ray sometimes has problems determining the location of the lesion because it is possible because of the large amount of noise in the image. Therefore, the X-ray results will be segmented images using the graph cut algorithm to analyze normal lungs and lungs infected with COVID-19. After obtaining the segmentation results in the form of binary images, the next step is to analyze using the box-counting method's fractal dimensions. From the fractal Dimension results, normal lungs have an average dimension of 1.7890, and lungs infected with COVID-19 have an average dimension of 1.5834. Normal lungs have dimensions larger than lungs infected with the coronavirus due to the lungs' covering by lesions or abnormal conditions in body tissues. This is what causes COVID-19 patients to have complaints of difficulty breathing.

Keywords


Box Counting; COVID-19; Fractal Dimension; Graph Cut; Image Segmentation

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Ait Skourt, B., El Hassani, A., & Majda, A. (2018). Lung CT image segmentation using deep neural networks. Procedia Computer Science, 127, 109–113. https://doi.org/10.1016/j.procs.2018.01.104

Awate, S. P., Garg, S., & Jena, R. (2019). Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach. Medical Image Analysis, 55, 181–196. https://doi.org/10.1016/j.media.2019.04.014

Ayad, A., Amrani, M., & Bakkali, S. (2019). Quantification of the Disturbances of Phosphate Series Using the Box-Counting Method on Geoelectrical Images (Sidi Chennane, Morocco). International Journal of Geophysics, 2019. https://doi.org/10.1155/2019/2565430

Boykov, Y. Y. (2001). Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. July, 105–112.

Cadena, L., Zotin, A., & Cadena, F. (2018). Enhancement of medical image using spatial optimized filters and OpenMP technology. Lecture Notes in Engineering and Computer Science, 2233, 324–329.

Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. Bin, Islam, K. R., Khan, M. S., Iqbal, A., Al-Emadi, N., & Reaz, M. B. I. (2020). Can AI help in screening viral and COVID-19 pneumonia? ArXiv.

Cimen, M. E., Boyraz, O. F., Yildiz, M. Z., & Boz, A. F. (2021). A new dorsal hand vein authentication system based on fractal dimension box counting method. Optik, 226, 165438. https://doi.org/10.1016/j.ijleo.2020.165438

Davies, N. A., Harrison, N. K., Keith Morris, R. H., Noble, S., Lawrence, M. J., D’Silva, L. A., Broome, L., Brown, M. R., Hawkins, K. M., Williams, P. R., Davidson, S., & Evans, P. A. (2015). Fractal dimension (df) as a new structural biomarker of clot microstructure in different stages of lung cancer. Thrombosis and Haemostasis, 114(6), 1251–1259. https://doi.org/10.1160/TH15-04-0357

Eriksson, A. P., Barr, O., & Kalle, A. (2006). Image Segmentation Using Minimal Graph Cuts. Swedish Symposium on Image Analysis, 45–48.

Feng-Ping, A., & Zhi-Wen, L. (2019). Medical image segmentation algorithm based on feedback mechanism convolutional neural network. Biomedical Signal Processing and Control, 53. https://doi.org/10.1016/j.bspc.2019.101589

Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., Thoma, G., Wang, Y. X., Lu, P. X., & McDonald, C. J. (2014). Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging, 33(2), 233–245. https://doi.org/10.1109/TMI.2013.2284099

Khotimah, C., & Juniati, D. (2018). Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension. Journal of Physics: Conference Series, 947(1). https://doi.org/10.1088/1742-6596/947/1/012004

Kim, T., Ahn, C., & Lee, O. (2018). Image segmentation by graph cut for radiation images of small animal blood vessels. Microscopy Research and Technique, 81(12), 1506–1512. https://doi.org/10.1002/jemt.23154

Kolmogorov, V., & Zabih, R. (2002). What energy functions can be minimized via graph cuts? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2352, 65–81. https://doi.org/10.1007/3-540-47977-5_5

Lei, L., Xi, F., Chen, S., & Liu, Z. (2021). Iterated graph cut method for automatic and accurate segmentation of finger-vein images. Applied Intelligence, 51(2), 673–689. https://doi.org/10.1007/s10489-020-01828-8

Li, Z., Liu, J., & Cheng, J. (2019). Exploiting Multi-Direction Features in MRF-Based Image Inpainting Approaches. IEEE Access, 7, 179905–179917. https://doi.org/10.1109/ACCESS.2019.2959382

Lin, X., Gong, Z., Xiao, Z., Xiong, J., Fan, B., & Liu, J. (2020). Novel coronavirus pneumonia outbreak in 2019: Computed tomographic findings in two cases. Korean Journal of Radiology, 21(3), 365–368. https://doi.org/10.3348/kjr.2020.0078

Mamatha, S. K., & Krishnappa, H. K. (2019). Graph Cut Based Multiple Interactive Image Segmentation for Medical Applications. 7(3), 567–571.

Mandelbrot, B. (1967). How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science, 156(3775), 636–638. https://doi.org/10.1126/science.156.3775.636

Nabeel, S. (2020). COVID-19 Patients Lungs X Ray Images 10000. https://www.kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images

Nayak, S. R., Mishra, J., Khandual, A., & Palai, G. (2018). Fractal dimension of RGB color images. Optik, 162, 196–205. https://doi.org/10.1016/j.ijleo.2018.02.066

Nayak, S. R., Mishra, J., & Palai, G. (2019). Analysing roughness of surface through fractal dimension: A review. Image and Vision Computing, 89, 21–34. https://doi.org/10.1016/j.imavis.2019.06.015

Peng, Z., Qu, S., & Li, Q. (2019). Interactive image segmentation using geodesic appearance overlap graph cut. Signal Processing: Image Communication, 78(April), 159–170. https://doi.org/10.1016/j.image.2019.06.012

Rahmadewi, R., & Kurnia, R. (2016). Klasifikasi Penyakit Paru Berdasarkan Citra Rontgen dengan Metoda Segmentasi Sobel. Jurnal Nasional Teknik Elektro, 5(1), 7. https://doi.org/10.25077/jnte.v5n1.174.2016

Rahman, T. (2020). COVID-19 Radiography Database. Kaggle. https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

Setiawaty, I., & Sinurat, S. (2017). Penerapan Algoritma Homogeneity untuk Deteksi Tepi Citra pada Citra Rontgen. Pelita Informatika Budi Darma, 16, 414–417.

Syakrani, N., Widhiyasana, Y., & Efendi, A. A. (2018). Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 7(1). https://doi.org/10.22146/jnteti.v7i1.398

World Health Organization. (2021). COVID-19 Weekly Epidemiological Update 22. World Health Organization, January, 1–3. https://www.who.int/docs/default-source/coronaviruse/situation-reports/weekly_epidemiological_update_22.pdf

Xia, K., Yin, H., Qian, P., Jiang, Y., & Wang, S. (2019). Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access, 7, 96349–96358. https://doi.org/10.1109/ACCESS.2019.2929270

Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology, 296(2), E41–E45. https://doi.org/10.1148/radiol.2020200343




DOI: http://dx.doi.org/10.35671/telematika.v14i1.1217

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Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


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