Registrasi Citra Dental Menggunakan Feature From Accelerated Segment Test dan Local Gabor Texture For Iterative Point Correspondence

Penulis

Ahmad Afif Supianto, Budi Darma Setiawan

Abstrak

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

Kata Kunci


registrasi citra; learning feature; local gabor texture; iterative point correspondence; citra dental periapikal

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Referensi


BYRD, V., TRACY, M., & REDDY, M. 1998. Semiautomated image registration for digital subtraction radiography. Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics : Mosby-Year Book, Inc., 85(4), 473-478.

CARRANZA, F. A. & CAMARGO, P. M. 2006. The Periodontal Pocket. Saunders : Carranza’s Clinical Periodontology, 10, 445-446.

DRAIDI, M. A. 2009. Differences in Amount and Architecture of Alveolar Bone Loss in Chronic and Aggressive Periodontitis Assessed Through Panoramic Radiographs. Pakistan Oral and Dental Journal, 29(1), 59-62.

ETTINGER G. J. 1994. Development of automated registration algorithms for subtraction radiography. Journal of Clinical Periodontology ed. 543 Blackwell Publishing Ltd., 21(8), 540.

JEONG, K. & MOON, H. 2011. Object Detection using FAST Corner Detector based on Smartphone Platforms. First ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, 23-25 May, Jeju Island, Korea. 111-115.

YU, L., HE, Z., & CAO, Q. 2010. Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models. Image and Vision Computing, 28, 177–187.

KARYBALI, I. G., PSARAKIS, E. Z., BERBERIDIS, K., & EVANGELIDIS, G. D. 2008. An efficient spatial domain technique for subpiksel image registration. Signal Processing: Image Communication, 23(4), 711-724.

KHOCHT, A. 2010. Screening for periodontal disease: radiographs vs. PSR. American Dental Association, 127, 749-756.

LEHMANN, T. M., GRONDAHL, K., GRONDAHL, H-G., SCHMITT, W., & SPITZER, K. 1998. Observer-independent registration of perspective projection prior to subtraction of in vivo radiographs. Dentomaxillofacial Radiology, 27, 140-150.

MARKAKI, V. E., ASVESTAS, P. A. & MATSOPOULOS G. K. 2009. An iterative point correspondence algorithm for automatic image registration: An application to dental subtraction radiography. Computer Methods and Programs in Biomedicine, 93(1), 61-72.

MOKO, Y., WATANABE, Y., KOMURO, T., ISHIKAWA, M., NAKAJIMA, M., & ARIMOTO, K. 2011. Implementation and Evaluation of FAST Corner Detection on the Massively Parallel Embedded Processor MX-G. Proc. IEEE Comput. Soc. Conf. CVPRW, 157 -162.

OKANO T. 1990. Digital subtraction of radiograph in evaluating alveolar bone changes after initial periodontal therapy. Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics, 69(2), 258-262.

OU, Y., SOTIRAS, A., PARAGIOS, N. & DAVATZIKOS, C. 2010. DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Medical Image Analysis, 15(4), 622-639.

ROSTEN, E., PORTER, R. & DRUMMOND, T. 2006. Machine learning for high-speed corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 105-119.

SERRA, J. 1982. Image Analysis and Mathematical Morphology, Academic Press, Inc., London.

SUPIANTO, A. A., ARIFIN, A. Z., & WIJAYA, A. Y. 2011. Nonsubsampled Contourlet Transform Dan Iterative Point Correspondence Untuk Registrasi Pada Citra Dental Periapikal. Prosiding Seminar Nasional Manajemen Teknologi XIV. Program Studi MMT-ITS, 23 Juli, Surabaya, Indonesia.

YI W-J., HEO, M-S., LEE, S-S., CHOI, S-C. & HUH, K-H. 2006. ROI-based image registration for digital subtraction radiography. Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics, 101(4), 523-529.

YONGXIN G., DAN, Y., JIWEN L., BO L., & XIAOHONG Z. 2013. Active appearance models using statistical characteristics of Gabor based texture representation. J. Vis. Commun. Image R, 24, 627-634.

ZACHARAKI, E. I., MATSOPOULOS, G. K., ASVESTAS, P. A., NIKITA1, K. S., GRONDHL, K. & GRONDAHL, H-G. 2004. A digital subtraction radiography scheme based on automatic multiresolution registration. Dentomaxillofacial Radiology : The British Institute of Radiology, 33, 1-33.

ZITOVÁ B. & FLUSSER J. 2003. Image registration methods: a survey. Image and Vision Computing, 21(11), 977-1000.




DOI: http://dx.doi.org/10.25126/jtiik.201744503