PENGUKURAN KETEBALAN TULANG KORTIKAL PADA CITRA PANORAMA GIGI BERBASIS MODEL

Authors

  • Dini Adni Navastara Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
  • Irna Dwi Anggraeni Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
  • Agus Zainal Arifin Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
Views: 1129 Downloads: 693 DOI: https://doi.org/10.12962/j24068535.v15i1.a641

Abstract

Pengukuran ketebalan tulang kortikal pada citra panorama gigi merupakan salah satu cara yang dapat digunakan untuk mendiagnosa osteoporosis. Ketebalan tulang kortikal pada gigi merupakan predictor penting untuk mengetahui kualitas kepadatan tulang. Namun, pengukuran ketebalan tulang kortikal pada citra panorama gigi masih dilakukan secara manual oleh ahli medis. Penelitian ini mengusulkan sebuah sistem otomatis untuk mengukur ketebalan tulang kortikal pada citra panorama gigi berbasis model profil. Pengukuran ketebalan tulang kortikal terdiri dari 5 tahapan yaitu ekstraksi fitur menggunakan multiscale line operator dan gradient orientation analysis pada citra Region Of Interest (ROI), segmentasi tulang kortikal, deteksi centerline pada tulang kortikal, pemodelan profil tulang kortikal, dan estimasi tebal tulang kortikal. Metode ini dievaluasi menggunakan 30 citra panorama gigi. Berdasarkan hasil uji coba, rata-rata akurasi segmentasi tulang kortikal pada ROI paling kiri, ROI kiri-tengah, ROI kanan-tengah, dan ROI paling kanan secara berurut-turut sebesar 95.41%, 89.96%, 95.12%, dan 93.50%. Persentase rata-rata selisih ketebalan tulang kortikal antara sistem dan ground truth menggunakan uji-t dengan 95% confidence interval sebesar 96.65%.

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References

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Published

2017-01-01

Issue

Vol 15, No. 1, Januari 2017

Section

Articles

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How to Cite

[1]
D. A. Navastara, I. D. Anggraeni, and A. Z. Arifin, “PENGUKURAN KETEBALAN TULANG KORTIKAL PADA CITRA PANORAMA GIGI BERBASIS MODEL”, JUTI, vol. 15, no. 1, pp. 119–127, Jan. 2017, doi: 10.12962/j24068535.v15i1.a641.