GABUNGAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX DAN GRAY LEVEL RUN LENGTH MATRIX PADA ANALISIS CITRA RADIOGRAFI DENTAL PANORAMIC UNTUK DETEKSI DINI OSTEOPOROSIS

نویسندگان

چکیده

ABSTRAKOsteoporosis merupakan salah satu masalah kesehatan utama. Osteoporosis dianggap sebagai penyakit metabolik yang umum, dan sering diabaikan. Penyakit ini kebanyakan menyerang wanita dewasa dapat menyebabkan kekurusan kerapuhan tulang, memicu patah tulang. didiagnosis dengan mengukur Densitas Mineral Tulang menggunakan DXA (dual energy X-ray absorptiometry). Perawatan alat membutuhkan biaya mahal, tidak tersedia secara luas. Sampel penelitian mengambil 19 orang kriteria inklusi perempuan telah menopause, dinyatakan sehat, mengalami tulang memiliki kelainan sejak lahir. diukur nilai bone mineral density (BMD) atau derajat osteoporosis DXA. Kemudian dilakukan pemotretan radiografi untuk mendapatkan citra dental panoramic. Tahapan adalah: 1) melakukan pre-processing terhadap panoramic mandibular; 2) menentukan tekstur metode gray level co-occurrence matrix 3) run length 4) mengkalisifikasikan k means kluster. Hasil Klasifikasi Kluster menunjukkan ketepatan klasifikasi sebesar 89,47% Kata kunci: radiografi; rahang; BMD; analisis tekstur. ABSTRACTOsteoporosis is one of the major health problems. considered a common metabolic disease, and often overlooked. This disease mostly affects adult women which can cause thin brittle bones, trigger fractures. diagnosed by measuring Bone Density using Treatment with this device expensive, it not widely available. The sample study took people inclusion criteria having declared healthy, had no fractures abnormalities since birth. was measured value or degree Then, radiography taken to obtain image. stages research are: radiographic image mandible; determine texture method classify cluster method.Classification results clusters show classification accuracy 89.47% Keywords:. Radiography; panoramic; analysis

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ژورنال

عنوان ژورنال: Orbita

سال: 2022

ISSN: ['2614-7017', '2460-9587']

DOI: https://doi.org/10.31764/orbita.v8i1.8334