Deteksi Glaukoma pada Citra Fundus Retina menggunakan Metode Local Binary Pattern dan Support Vector Machine

FEBI NURFAJAR, RITA MAGDALENA, SOFIA SA’IDAH

Sari


ABSTRAK

Glaukoma merupakan sebuah penyakit yang menyerang indera penglihatan dan dapat mengakibatkan kebutaan yang bersifat permanen. Meskipun penyakit ini tidak bisa disembuhkan, tetapi gejala kerusakannya dapat diminimalkan dengan melakukan pendeteksian secara dini. Deteksi glaukoma dapat dilakukan secara manual oleh oftalmologis, tetapi metode ini terbilang subyektif sebab hasil pengamatannya bergantung pada domain pengetahuan dokter, sementara di sisi lain teknik pencitraan medis modern, seperti OCT, CSLO, dan HRT berbiaya tinggi dan ketersediaan perangkatnya relatif terbatas. Pada penelitian ini, sebuah sistem berbasis machine learning untuk mendeteksi glaukoma pada citra fundus retina telah dirancang melalui proses pengolahan citra digital menggunakan metode Local Binary Pattern dan Support Vector Machine. Performansi sistem diuji pada 146 citra fundus yang terdiri dari citra fundus sehat dan glaukoma. Dengan menggunakan metode yang diusulkan, sistem mampu memberikan tingkat akurasi terbaik pada 93.15%, sensitivitas 92.30%, dan spesifisitas 93.61%.

Kata kunci: Glaukoma, Local Binary Pattern, Support Vector Machine

 

ABSTRACT

Glaucoma is a disease that attacks the sense of sight and can lead to permanent blindness. Although this disease cannot be cured, the symptoms of the damage can be minimized by early detection. Glaucoma detection can be done manually by an ophthalmologist, but this method is somewhat subjective because the results of the observations depend on the domain of the doctor’s knowledge, whereas onthe other hand,  modern medical imaging techniques, such as OCT, CSLO, and HRT, are high in cost and the availability of devices is relatively limited. This research proposed a machine learning-based system to detect glaucoma in retinal fundus images through digital image processing using the Local Binary Pattern and Support Vector Machine methods. The performance of the system was tested on 146 fundus images consisting of healthy fundus images and glaucoma. By using the proposed method, the system provide the best accuracy rate at 93.15%, sensitivity 92.30%, and specificity 93.61%.

Keyword: Glaucoma, Local Binary Pattern, Support Vector Machine


Kata Kunci


Glaukoma; Local Binary Pattern; Support Vector Machine

Teks Lengkap:

PDF

Referensi


Ahn, J. M., Kim, S., Ahn, K. S., Cho, S. H., Lee, K. B., & Kim, U. S. (2018). A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS ONE, 13(11), 1–8.

Al Rivan, M. E., & Juangkara, T. (2019). Identifikasi Potensi Glaukoma dan Diabetes Retinopati Melalui Citra Fundus Menggunakan Jaringan Syaraf Tiruan. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 6(1), 43–48.

Batista, F. J. F., Diaz-Aleman, T., Sigut, J., Alayon, S., Arnay, R., & Angel-Pereira, D. (2020). RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning. Image Analysis & Stereology, 39(3), 161–167.

Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and ohter kernel-based learning methods. Cambridge University Press.

Dey, A., & Bandyopadhyay, S. (2016). Automated Glaucoma Detection Using Support Vector Machine Classification Method. British Journal of Medicine and Medical Research, 11(12), 1–12.

Horwath, J. P., Zakharov, D. N., Mégret, R., & Stach, E. A. (2020). Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images. npj Computational Materials, 6(1), 1–9.

InfoDATIN Kementerian Kesehatan RI. (2019). Situasi Glaukoma di Indonesia (hal. 1–9).

Irawan, S., Hasan, Y., & Tampubolon, K. (2019). PENERAPAN METODE CLAHE UNTUK MEMPERJELAS OBJEK PANTULAN KACA PADA CITRA DIGITAL. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1).

Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017). Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition, 62, 135–160.

Men, S., Yan, L., Liu, J., Qian, H., & Luo, Q. (2017). A classification method for seed viability assessment with infrared thermography. Sensors, 17(4), 845.

Munarto, R., Permata, E., & Ginanjar, I. A. T. (2016). Klasifikasi Glaukoma Menggunakan Cup to Disk Ratio dan Neural Network. Simposium Nasional RAPI XV – 2016 FT UMS, (pp. 370–378).

Mustofa, A., Tjandrasa, H., & Amaliah, B. (2016). Deteksi Penyakit Glaukoma pada Citra Fundus Retina Mata Menggunakan Adaptive Thresholding dan Support Vector Machine. Jurnal Teknik ITS, 5(2).

Nanda, M. A., Boro Seminar, K., Nandika, D., & Maddu, A. (2018). A comparison study of kernel functions in the support vector machine and its application for termite detection. Information, 9(1), 5.

Nanni, L., Lumini, A., & Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. Artificial intelligence in medicine, 49(2), 117–125.

Nguyen, L. (2017). Tutorial on support vector machine. Appl. Comput. Math, 6, 1–15.

Prakasa, E. (2016). Ekstraksi Ciri Tekstur dengan Menggunakan Local Binary Pattern Texture Feature Extraction by Using Local Binary Pattern. 9(2), 45–48.

Song, K.-C., Yan, Y.-H., Chen, W.-H., & Zhang, X. (2013). Research and Perspective on Local Binary Pattern. Acta Automatica Sinica, 39(6), 730–744.

Tham, Y. C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C. Y. (2014). Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology, 121(11), 2081–2090.

Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer-Verlag.

Wedianto, A., & Sari, H. L. (2016). Analisa Perbandingan Metode Filter Gaussian, Mean dan Median Terhadap Reduksi Noise. Jurnal Media Infotama, 12(1).

Weinreb, R. N., Aung, T., & Medeiros, F. A. (2014). The pathophysiology and treatment of glaucoma: A review. JAMA - Journal of the American Medical Association, 311(18), 1901–1911.

Xiong, L., Li, H., & Zheng, Y. (2014). Automatic detection of glaucoma in retinal images. Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 81271650, (pp. 1016–1019).




DOI: https://doi.org/10.26760/elkomika.v10i4.769

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License