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
  • Sofia Debi Puspa Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Supriyadi Fakultas Teknologi Industri, Universitas Trisakti, Jakarta, Indonesia
  • Fayza Nayla Riyana 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%.

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Published

2024-05-08 — Updated on 2024-05-08

Issue

Vol. 9 No. 1 (2024)

Section

Article

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Copyright (c) 2024 Joko Riyono, Christina Eni Pujiastuti, Sopia Debi Puspa, Supriyadi, Fayza Nayla Riyani Putri

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