Registrasi Citra Dental Menggunakan Feature From Accelerated Segment Test dan Local Gabor Texture For Iterative Point Correspondence
DOI:
https://doi.org/10.25126/jtiik.201744503Kata Kunci:
registrasi citra, learning feature, local gabor texture, iterative point correspondence, citra dental periapikalAbstrak
Abstrak
Registrasi citra di bidang periodontal telah dikembangkan untuk melakukan evaluasi terhadap tulang alveolar. Masalah yang disebabkan oleh kesalahan saat ekstraksi fitur atau oleh degradasi gambar bisa timbul pada proses pencocokan fitur. Selain itu, teknik registrasi citra yang didasarkan pada fitur seperti titik, identifikasi tepian (edges), kontur, atau fitur yang lain yang biasa digunakan untuk membandingkan gambar dan kemudian memetakannya merupakan teknik yang sangat sensitif terhadap keakuratan pada tahap ekstraksi fitur. Dari kedua argumen ini, maka diperlukan teknik ekstraksi fitur yang tangguh untuk mencegah terjadinya kesalahan pada proses pencocokan fitur sehingga mendapatkan hasil registrasi citra yang akurat. Pada penelitian ini, diusulkan metode baru untuk registrasi citra. Metode yang diusulkan menggunakan metode ekstraksi fitur yang efektif terhadap akurasi dan efisien terhadap waktu komputasi dengan menerapkan Learning Features, yaitu Feature from Accelerated Segment Test (FAST) sebagai metode ekstraksi fitur. Selain itu, akan dilakukan pengembangan terhadap proses pencocokan fitur dengan menerapkan Local Gabor Texture (LGT) pada algoritma Iterative Point Correspondence (IPC) untuk melakukan registrasi pada citra dental periapikal. Uji coba dilakukan terhadap 8 citra grayscale dental periapikal dan berhasil melakukan registrasi citra pada citra dental periapikal dengan nilai akurasi rata-rata diatas 93% dengan jumlah iterasi minimal mulai dari 400 iterasi.
Kata kunci: registrasi citra, learning feature, local gabor texture, iterative point correspondence, citra dental periapikal
Abstract
Image registration in the periodontal field has been developed to evaluate alveolar bones. Problems caused by errors during feature extraction or by image degradation can arise in feature matching process. In addition, image registration techniques that are based on features such as points, identification of edges, contours, or other features commonly used to compare images and map them are very sensitive techniques for accuracy at the feature extraction stage. From both of these arguments, a robust feature extraction technique is needed to prevent mistakes in the feature matching process to get image registration results accurately. In this study, a new method for image registration is proposed. The proposed method uses an effective feature extraction method for accuracy and efficient computing time by applying learning features, which is Feature from Accelerated Segment Test (FAST) as a feature extraction method. In addition, a feature-matching process will be developed by applying Local Gabor Texture (LGT) to the Iterative Point Correspondence (IPC) algorithm to register on the periapical dental images. The experiments were conducted on 8 grayscale dental periapical images and successfully registered the image in periapical dental image with an average accuracy more than 93% with a minimum iteration count starting from 400 iterations.
Keywords: image registration, learning feature, local gabor texture, iterative point correspondence, dental periapical images
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