Simulasi Deteksi Tonsilitis Mengunakan Pengolahan Citra Digital Berdasarkan Warna dan Luasan pada Tonsil

  • Sang Made Lanang Prasetya Universitas Telkom
  • Achmad Rizal Universitas Telkom
  • I Nyoman Apraz Ramatryana Universitas Telkom
Keywords: deteksi, tonsilitis, tonsil, histogram, ROI, k-NN

Abstract

Tonsillitis or known as tonsils is a medical condition characterized by inflammation of the tonsils, causing sore throat, difficulty swallowing, fever, and in certain cases can lead to heart attack or pneumonia. Doctors diagnose tonsillitis in a visual way, see tonsil inflammation and assess subjectively. This study designed a tool to calculate the area of inflamed areas that can be used to help doctors diagnose tonsillitis. Tonsils image processed on the red layer to quantify the extent of tonsils. Furthermore, the red area was calculated as area ofinflammation. In next stage, find the feature extraction using histogram analysis to find the distribution of image intensity levels. The results were classified using k-Nearest Neighbor (k-NN). From 64 datas which consists of 32 normal and 32 tonsillitis, a system can reach 90,625% accuracy rate. This value is achieved at the cityblock distance measurement and k = 1.

References

I. Megantara, "Informasi Kesehatan THT," 7 Juni 2008. [Online]. Available: imammegantara.blogspot.com. [Accessed 11 Maret 2014].

M. Hammouda, " Chronic Tonsillitis Bacteriology in Egyptian Children Including Antimicrobial Susceptibility," Department of ENT, Department of Medical Microbiology and Immunology,Faculty of Medicine, Cairo University and Department of Pediatrics, Research Institute of Ophtalmology, Giza, Egypt, Australian Journal of Basic and Applies Sciences , vol. 3(3), pp. 1948-1953, 2009.

Farokah, "Hubungan Tonsilitis Kronik dengan Prestasi Belajar pada Siswa Kelas II Sekolah Dasar di Kota Semarang," Bagian Ilmu Kesehatan THT-KL Fakultas Kedokteran Universitas Diponegoro, SMF Kesehatan THT-KL Rumah Sakit Dr. Kariadi Semarang, Indonesia, Cermin Dunia Kedokteran No 155, pp. 87-92, 2007.

P. Phensadsaeng, P. Kumhom, and K. Chamnongthai. “A Computer-aided-Diagnosis of Tonsillitis Using Tonsi size and Color”. IEEE. ISCAS 2006, Greece, pp.5563-5566, 2006.

N. A. Apriliani, "Scribd," 17 November 2013. [Online]. Available: id.scribd.com/doc/184832842/laporan-pendahuluan-Tonsilitis#scribd. [Accessed 11 Maret 2014].

A. Leelasantitham, S. Kiattisin. “A Diagnosis of Tonsillitis using Image Processing and Neural Network”. International Journal of Applied Biomedical Engineering, vol. 2 (2), pp. 36-42, 2009.

G. L. Adams, Buku Ajar Penyakit THT, Jakarta: EGC, 1997

P. Cunningham, S. J. Delany, "k-Nearest Neighbour Classifiers," Technical Report UCD-CSI, vol. 4, pp. 1-2, 2007.

Published
2015-06-04
How to Cite
Sang Made Lanang Prasetya, Achmad Rizal, & I Nyoman Apraz Ramatryana. (2015). Simulasi Deteksi Tonsilitis Mengunakan Pengolahan Citra Digital Berdasarkan Warna dan Luasan pada Tonsil. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 4(1), 45-49. Retrieved from https://dev.journal.ugm.ac.id/v3/JNTETI/article/view/3033
Issue
Vol 4 No 1: February 2015
Section
Articles

© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.