Published: 2022-01-03
Application of Spinal Disorders Detection on X-Ray Images Using Segmentation and K-Means Clustering
DOI: 10.35870/jtik.v6i3.456
Muhammad Khairul, Fauziah Fauziah, Iskandar Fitri
-
Muhammad Khairul:
Nasional University
-
Fauziah Fauziah:
Nasional University
-
Iskandar Fitri:
Nasional University
Abstract
There are three types of spinal disorders, namely kyphosis, lordosis, and scoliosis. To find out spinal disorders, it is necessary to carry out X-rays from an early age. Spinal disorders are not only found in children but can be found in adolescents, adults, and the elderly. Along with the times, making information technology more sophisticated is the advancement of image processing technology. Image processing can help in the medical field to analyze X-ray results to diagnose internal disorders or diseases. This study makes an application for the detection of spinal disorders with several methods of image segmentation processes and using the k-means clustering algorithm on x-ray images of spinal disorders. This segmentation image processing stage requires five stages of processing including cropping, resizing, median filter, histogram equalization, thresholding, and binary edges, and k-means clustering process as a comparison. This application is expected to be useful in knowing the difference between spinal disorders of lordosis, kyphosis, and scoliosis
Keywords
Spinal disorders; X-ray; Image processing; Image Segmentation; K-means Clustering
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 6 No. 3 (2022)
-
Section: Computer & Communication Science
-
Published: 2022-01-03
-
License: CC BY 4.0
-
Copyright: © 2022 Authors
-
DOI: 10.35870/jtik.v6i3.456
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem.
Muhammad Khairul, Nasional University
Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Indonesia.
Fauziah Fauziah, Nasional University
Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Indonesia.
Iskandar Fitri, Nasional University
Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Indonesia.
-
Hasmyati & Aprilo, I. 2014. Analisis Pengukuran Foremetric Dan Myoline Atlet Pplm Universitas Negeri Makassar. Cakrawala Pendidikan, Juni 2014, Th. XXXIII, No. 2.
-
Helmi, Z.N. 2013. "Buku Ajar Gangguan Muskuloskeletal". Jakarta: Salemba Medika.
-
Suryabi, C.L. 2015. "Perancangan Media Pembelajaran Interaktif Tentang Sikap Tubuh Yang Benar". DKV, Fakultas Seni dan Desain, Universitas Kristen Petra.
-
Suratun, dkk. 2008. "Klien Gangguan Muskuloskeletak: Seri Asuhan Keperawatan". Jakarta: EGC
-
Suryani, Dwi. 2018. "Analisis Gelombang Elektromagnetik (Sinar-X) Dalam Kesehatan Perspektif Al-Qur’an Dan Sains". PROSIDING Seminar Nasional Pendidikan Fisika FITK, UNSIQ ISSN 2615-2789 Vol. 1, No. 1.
-
Palimbong, E.Y. 2018. â€Deteksi Tbc Paru Dari Citra Foto Rontgen Menggunakan Jaringan Syaraf Tiruan Backpropagationâ€. Teknik Elektro Fakultas Teknik Universitas Semarang.
-
P, D.A., Arista, V., dkk. 2021. â€Segementasi Citra Digital Ikan Menggunakan Metode Thresholding Dan K-Meansâ€.
-
Priyawati, D., Soesanti, Indah., & Hidayah, I. 2015. â€Kajian Pustaka Metode Segmentasi Citra Pada Mri Tumor Otakâ€. Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada.
-
Atina. 2017. Segmentasi Citra Paru Menggunakan Metode k-Means Clustering. Universitas PGRI Palembang.
-
Torkis Nasution. 2012. â€Segmentasi Citra Digital Berbasis Clustering Menggunakan Deteksi Tepi Sobelâ€. Program Studi Teknik Informatika, STMIK-AMIK Riau.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.