Klasifikasi Analisis Tekstur Citra Radiografi Panoramik Gigi Untuk Deteksi Osteoporosis Menggunakan Metode K-Nearest Neighbor (KNN)

Yudhi Diputra; Syafrijon Syafrijon; Emilham Mirshad

doi:10.24036/voteteknika.v11i3.124871

Klasifikasi Analisis Tekstur Citra Radiografi Panoramik Gigi Untuk Deteksi Osteoporosis Menggunakan Metode K-Nearest Neighbor (KNN)

Yudhi Diputra⁽¹⁾, Syafrijon Syafrijon⁽²⁾, Emilham Mirshad⁽³⁾,
(1) Universitas Negeri Padang

Indonesia
(2) Universitas Negeri Padang

Indonesia
(3) Universitas Negeri Padang

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v11i3.124871

Full Text:

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Abstract

Osteoporosis merupakan salah satu penyakit yang berkaitan dengan proses penuaan (degeneratif) yang diindikasikan dengan terjadinya penurunan kerapatan yang cepat dan penipisan jaringan tulang sehingga terjadi penurunan kekuatan mekanik tulang dalam mendukung aktivitas sehari-hari. Teknik yang banyak dikembangkan untuk pengukuran yang berkaitan dengan massa tulang, serta dianggap sebagai gold standard adalah Dual Energi X-ray Absorptiometry (DXA). Beberapa penelitian sebelumnya memberikan peluang pemanfaatan citra radiografi panoramik gigi untuk analisis kerapatan trabekula tulang mandibula. Hasil ini digunakan sebagai sarana dekteksi dini kondisi Bone Mineral Density (BMD). Penelitian ini bertujuan melakukan klasifikasi terhadap hasil analisis tekstur menggunakan prinsip Grey Level Co-occurence Matrix (GLCM) pada citra panoramik gigi. Dari metode GLCM diperoleh ekstraksi fitur yang selanjutnya dijadikan input bagi K-Nearest Neighbor (KNN) untuk melakukan klasifikasi. Uji coba dilakukan menggunakan data BMD vertebra lumbar dan citra panoramik gigi 23 sampel wanita berusia antara 52 – 73 tahun yang telah memasuki masa postmenopause. Hasil klasifikasi kelas normal dan osteoporosis menggunakan KNN (9 data latih dan 14 data uji) memberikan pengenalan paling baik dengan akurasi 78,57%, sensitivitas (tingkat benar positif) 100% dan spesifisitas (tingkat benar negatif) 66,67%. Pengenalan paling baik didapatkan menggunakan fitur contrast, energy, dan homogeneity dan kombinasi ketiganya sebagai input bagi klasifikasi KNN.

Kata kunci : Osteoporosis, citra radiografi panoramik, analisis tekstur, Grey Level Co-occurrence Matrix (GLCM), K-Nearest Neighbor (KNN).

Osteoporosis is a degenerative disease which is characterized by a rapid decrease in bone density resulting in a decrease in the mechanical strength of the bones to support daily activities. The gold standard for bone mass measurement is Dual Energy X-ray Absorptiometry (DXA). Several previous studies provided the opportunity to utilize dental panoramic radiographic images for analysis of mandibular trabecular bone density. These results are used as an early detection tool for Bone Mineral Density (BMD) conditions. This research tries to use the feature extraction results of texture analysis using Grey Level Co-occurrence Matrix (GLCM) on dental panoramic images as input for K-Nearest Neighbor (KNN) to carry out classification. The trial was conducted using lumbar vertebral BMD data and dental panoramic images from 23 samples of women aged between 52-73 years who had entered the postmenopausal period. Classification results of normal and osteoporosis using KNN (9 training data and 14 test data) gave the best recognition with an accuracy of 78.57%, a sensitivity of 100% and a specificity of 66.67%. The best recognition is obtained by using contrast, energy, and homogeneity features and their combination as input for KNN classification.

Keywords: Osteoporosis, dental panoramic radiograph, texture analysis, Grey Level Co-occurrence Matrix (GLCM), K-Nearest Neighbor (KNN) .

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