Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images

Authors

  • Joko Riyono Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Christina Eni Pujiastuti Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Sopia Debi Puspa Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Supriyadi Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Fayza Nayla Riyani Putri Information System, Universitas Diponegoro, Semarang, Indonesia

DOI:

https://doi.org/10.15575/join.v9i1.1178

Keywords:

Canny, Chan-Vese, K-Means Clustering, Lungs, Segmentation, X-Ray Image

Abstract

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.

References

Niederman, M. S., & Cilloniz, C. (2022). Aspiration pneumonia. Revista Espanola de Quimioterapia, 35. https://doi.org/10.37201/req/s01.17.2022

Smithard, D. G., & Yoshimatsu, Y. (2022). Pneumonia, Aspiration Pneumonia, or Frailty-Associated Pneumonia? Geriatrics (Switzerland), 7(5). https://doi.org/10.3390/geriatrics7050115

Natarajan, A., Beena, P. M., Devnikar, A. V., & Mali, S. (2020). A systemic review on tuberculosis. In Indian Journal of Tuberculosis (Vol. 67, Issue 3). https://doi.org/10.1016/j.ijtb.2020.02.005

Kiani, D. (2023). X-Ray Diffraction (XRD). In Springer Handbooks. https://doi.org/10.1007/978-3-031-07125-6_25

Patil, B. M., & Burkpalli, V. (2022). Segmentation of cotton leaf images using a modified chan vese method. Multimedia Tools and Applications, 81(11). https://doi.org/10.1007/s11042-022-12436-8

Song, J., Pan, H., Liu, W., Xu, Z., & Pan, Z. (2021). The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2020.3047848

Wang, Z., Wang, K., Yang, F., Pan, S., & Han, Y. (2018). Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator. Information Processing in Agriculture, 5(1). https://doi.org/10.1016/j.inpa.2017.09.005

Zheng, D., Bao, C., Shi, Z., Ling, H., & Ma, K. (2022). Unsupervised Deep Learning Meets Chan-Vese Model. CSIAM Transactions on Applied Mathematics, 3(4). https://doi.org/10.4208/csiam-am.SO-2021-0049

Sekehravani, E. A., Babulak, E., & Masoodi, M. (2020). Implementing canny edge detection algorithm for noisy image. Bulletin of Electrical Engineering and Informatics, 9(4). https://doi.org/10.11591/eei.v9i4.1837

Pradeep Kumar Reddy, R., & Nagaraju, C. (2019). Improved canny edge detection technique using S-membership function. International Journal of Engineering and Advanced Technology, 8(6). https://doi.org/10.35940/ijeat.E7419.088619

Huang, M., Liu, Y., & Yang, Y. (2022). Edge detection of ore and rock on the surface of explosion pile based on improved Canny operator. Alexandria Engineering Journal, 61(12). https://doi.org/10.1016/j.aej.2022.04.019

Pratiwi, E. H., & Juniati, D. (2022). CLUSTERING OF LUNG DISEASE BASED ON CHEST X-RAY USING DIMENSIONS FRACTAL BOX COUNTING AND K-MEDOIDS. Jurnal Riset Dan Aplikasi Matematika (JRAM), 6(1), 1–12. https://doi.org/10.26740/JRAM.V6N1.P1-12

Bookstaver, M. (2021). Secondary Data Analysis. In The Encyclopedia of Research Methods in Criminology and Criminal Justice: Volume II: Parts 5-8. https://doi.org/10.1002/9781119111931.ch107

Ferreira, W. D., Ferreira, C. B. R., da Cruz Júnior, G., & Soares, F. (2020). A review of digital image forensics. Computers and Electrical Engineering, 85. https://doi.org/10.1016/j.compeleceng.2020.106685

Sundani, D., Widiyanto, S., Karyanti, Y., & Wardani, D. T. (2019). Identification of image edge using quantum canny edge detection algorithm. Journal of ICT Research and Applications, 13(2). https://doi.org/10.5614/itbj.ict.res.appl.2019.13.2.4

Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2988796

Rao, B. S. (2020). Dynamic Histogram Equalization for contrast enhancement for digital images. Applied Soft Computing Journal, 89. https://doi.org/10.1016/j.asoc.2020.106114

Baskar, A., Rajappa, M., Vasudevan, S. K., & Murugesh, T. S. (2023). Digital Image Processing. In Digital Image Processing. https://doi.org/10.1201/9781003217428

Agrawal, S., Panda, R., Mishro, P. K., & Abraham, A. (2022). A novel joint histogram equalization based image contrast enhancement. Journal of King Saud University - Computer and Information Sciences, 34(4). https://doi.org/10.1016/j.jksuci.2019.05.010

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2024-05-08 — Updated on 2024-05-08

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