Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi

Penulis

  • Safri Adam Institut Teknologi Sepuluh Nopember Surabaya
  • Agus Zainal Arifin Institut Teknologi Sepuluh Nopember Surabaya

DOI:

https://doi.org/10.25126/jtiik.0813013

Abstrak

Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA , dan yang terakhir segmentasi menggunakan metode level set. Hasil evaluasi menunjukkan bahwa strategi yang diusulkan berhasil melakukan inisialisasi secara otomatis dengan akurasi 73%. Hasil evaluasi segmentasi objek overlap cukup memuaskan dengan rasio misclassification error  0,93% dan relative foreground area error 24%. Dari hasil evaluasi menunjukkan bahwa strategi yang diusulkan dapat melakukan inisialisasi otomatis dengan baik. Inisialisasi yang tepat menghasilkan segmentasi yang baik pada metode level set.


Abstract

Individual teeth segmentation has done a lot of the recent research and obtained good results. When faced with overlapping teeth, this is quite challenging. To separate overlapping teeth, it is necessary to extract the overlapping object first. The level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. This research proposes an automatic initialization strategy for the level set method to segment overlapping teeth using Hierarchical Cluster Analysis on dental panoramic radiograph images. The proposed strategy stage consists of preprocessing where there are several processes of enhancement, rotation, and cropping of the image, Then the automatic initialization process uses the HCA algorithm and the last is segmentation using the level set method. The evaluation results show that the proposed strategy is successful in carrying out automatic initialization with an accuracy of 73%. The results of the overlap object segmentation evaluation are satisfactory with a misclassification error ratio of 0.93% and a relative foreground area error of 24%. The evaluation results show that the proposed strategy can carry out automated initialization well. Proper initialization results can perform good segmentation of the level set method.


Downloads

Download data is not yet available.

Referensi

ARIFIN, A.Z., ADAM, S., MOHAMMAD, A.M., ANGGRIS, F., INDRASWARI, R. AND NAVASTARA, D.A., 2019. Detection of Overlapping Teeth on Dental Panoramic Radiograph. International Journal of Intelligent Engineering and Systems, 12(6), pp.71–80.

ARIFIN, A.Z. AND ASANO, A., 2006. Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recognition Letters, 27(13), pp.1515–1521.

FAN, W., WANG, K., CAYRE, F. AND XIONG, Z., 2015. Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution. IEEE Transactions on Information Forensics and Security, 10(5), pp.1076–1091.

FARIZA, A., 2019. Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm. 12(3).

INDRASWARI, R., ARIFIN, A.Z., NAVASTARA, D.A. AND JAWAS, N., 2016. Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding. Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015, pp.49–54.

INDRASWARI, R., ZAINAL, A., NANIK, A., EHA, S. AND ASTUTI, R., 2019. Automatic Segmentation of Mandibular Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting. 12(4), pp.130–141.

KURNIAWAN, R., MUHIMMAH, I., KURNIAWARDHANI, A. AND INDRAYANTI, I., 2018. Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation. CommIT (Communication and Information Technology) Journal, 11(1), p.25.

LI, C., XU, C., MEMBER, S., GUI, C. AND FOX, M.D., 2010. Distance Regularized Level Set Evolution and Its Application to Image Segmentation. IEEE Transactions on Image Processing, 19(12), pp.3243–3254.

LIN, P.L., HUANG, P.Y., HUANG, P.W., HSU, H.C. AND CHEN, C.C., 2014. Teeth segmentation of dental periapical radiographs based on local singularity analysis. Computer Methods and Programs in Biomedicine, [online] 113(2), pp.433–445.

LIN, P.L., LAI, Y.H. AND HUANG, P.W., 2010. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, [online] 43(4), pp.1380–1392.

LU, Z., CARNEIRO, G., BRADLEY, A.P. AND MEMBER, S., 2015. An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells. 11(4).

MENON, H.P. AND RAJESHWARI, B., 2016. Enhancement of Dental Digital X-Ray Images based On the Image Quality BT - Intelligent Systems Technologies and Applications 2016. In: J.M. Corchado Rodriguez, S. Mitra, S.M. Thampi and E.-S. El-Alfy, eds. Cham: Springer International Publishing.pp.33–45.

NA, S.D., LEE, G., LEE, J.H. AND KIM, M.N., 2014. Individual tooth region segmentation using modified watershed algorithm with morphological characteristic. Bio-Medical Materials and Engineering, 24(6), pp.3303–3309.

OTSU, N., 1979. A Threshold Selection Method from Gray-Level Historams. IEEE Transactions on Systems, Man and Cybernetics, 20(1), pp.62–66.

POONSRI, A., AIMJIRAKUL, N., CHAROENPONG, T. AND SUKJAMSRI, C., 2017. Teeth segmentation from dental x-ray image by template matching. BMEiCON 2016 - 9th Biomedical Engineering International Conference, pp.1–4.

RAZALI, MUHAMMAD RIZAL, AHMAD, NAZATUL SABARIAH, ROZITA, H., ZAKI, Z. AND ISMAIL, W., 2014. Sobel And Canny Edges Segmentations For The Dental Age Assessment. In: International Conference on Computer Assisted System in Health Sobel.

RIANA, D., HIDAYANTO, A.N., WIDYANTORO, D.H., MENGKO, T.L.R. AND KALSOEM, O., 2018. Segmentation of overlapping cytoplasm and overlapped areas in Pap smear images. 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, 2018-Janua, pp.1–5.

SONG YUHEN, Y.H., 2012. Image Segmentation Algorithms Overview. Architectures and Algorithms for Digital Image Processing II, 0534, p.172.

T. LOTFI MAHYARI, R.M.D., 2017. Random Walks For Image Segmentation Containing Translucent Overlapped Objects. In: IEEE Global Conference on Signal and Information Processing. pp.46–50.

WANG, Z., WANG, K., YANG, F., PAN, S. AND HAN, Y., 2018. Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator. Information Processing in Agriculture, [online] 5(1), pp.1–10.

YADOLLAHI, M., PROCHÁZKA, A., KAŠPAROVÁ, M. AND VYŠATA, O., 2015. Separation of overlapping dental objects using normal vectors to image region boundaries. 2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015, pp.3–6.

Diterbitkan

15-06-2021

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi. (2021). Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), 429-438. https://doi.org/10.25126/jtiik.0813013