Optic Disc Detection on Retina Image using Extreme Learning Machine
DOI:
10.33395/sinkron.v8i2.12123Keywords:
Retina image; optic disk detection; ELM; histogram; Diabetic Macular Edema (DME)Abstract
Optic disk detection on retina image has become one of many initial steps in evaluation of Diabetic Macular Edema (DME) severity. As much as early the step is, the result of the step is extremely essential. This article discusses the optic disk detection on retina image based on the color histogram value. The detection is done by using color histogram value which is taken from window sliding process with the size of 50x50 pixels. First, the candidates of optic disc were detected using Extreme Learning Machine towards the histogram value. Then the optic disc was selected form the candidates of optic which has highest average intensity. 4 retina image datasets were employed in the evaluation, including Drions dataset, DRIVE dataset, DiaretDB1 dataset, and Messidor dataset. The result of evaluation then validated by medical expert. The model outcome reaches the accuracy as much as 85,39 % for DiaretDB1 dataset, 95% for DRIVE dataset, 98,18% for Drions and 99% for Messidor dataset.
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Al., L. P. A. et. (2015). Diabetic Retinopathy. Medicine (Baltimore), 43(1), 13–19.
Al Shalchi, N. F. A., & Rahebi, J. (2022). Human retinal optic disc detection with grasshopper optimization algorithm. Multimedia Tools and Applications, 81, 24937–24955.
Alghadyan, A. A. (2015). Diabetic Retinopathy - An Update. J. Int. Med. Sci. Acad, 28(1), 54–58.
Anam, K., & Al-Jumaily, A. (2017). Evaluation of Extreme Learning Machine for Classification of Individual and Combined Finger Movements using Electromyography on Amputees and Non-Amputees. Neural Networks, 85, 51–68.
Carmona, E. J., Rincón, M., García-Feijoo, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243–259.
Chacko, B. P., Krishnan, V. R. V., Raju, G., & Anto, P. B. (2012). Handwritten Character Recognition using Wavelet Energy and Extreme Learning Machine. International Journal of Machine Learning and Cybernetics, 3(2), 149–161.
Cirneanu, S., Ichim, L., & Popescu, D. (2017). Accurate Localization of the Optic Disc Based on LBP Descriptors. 40th International Conference on Telecommunications and Signal Processing (TSP), 678–681.
Ciulla, T. A., Amador, A. G., & Zinman, B. (2003). Diabetic Retinopathy and Diabetic Macular Edema: Pathophysiology, Screening, and Novel Therapies. Diabetes Care, 26(9), 2653–2664.
Decencière, E. (2014). Feedback on a publicly distributed image database: The Messidor database. Image Anal. Stereol, 33(3), 231–234.
Fleming, A. D., Goatman, K. A., Philip, S., Olson, J. A., & Sharp, P. F. (2007). Automatic Detection of Retinal Anatomy to Assist Diabetic Retinopathy Screening. Physic in Medicine & Biology, 52(2), 331–345.
Huang, G. Bin, Zhu, Q. Y., & Siew, C. K. (2006). Extreme Learning Machine: Theory and Applications. Neurocomputing, 70(1–3), 489–501.
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 617–623.
J, P., & B, V. K. (2022). Optic Disc Detection in Fundus Eye Images – A Detailed Survey. ICCAP 2021. https://doi.org/10.4108/eai.7-12-2021.2314560
Kakade, P., Kale, A., Jawade, I., Jadhav, R., & Kulkarni, N. (2021). Optic Disc Detection using Image Processing and Deep Learning. International Journal of Computing and Digital Systems, 3(3), 1–8. https://journal.uob.edu.bh/bitstream/handle/123456789/4435/1570702812.pdf?sequence=1
Kauppi, T. (2007). DIARETDB1 diabetic retinopathy database and evaluation protocol. Procedings Br. Mach. Vis. Conf., 15.1-15.10.
Lalonde, M., Beaulieu, M., & Gagnon, L. (2001). Fast and Robust Optic Disc Detection using Pyramidal Decomposition and Hausdorff-Based Template Matching. IEEE Transactions on Medical Imaging, 20(11), 1193–1200.
Listyalina, L., & Dharmawan, D. A. (2019). Detection of Optic Disc Centre Point in Retinal Image. Journal of Electrical Technology UMY, 3(1), 19–23. https://doi.org/10.18196/jet.3150
Martinez-Perez, M. E., Witt, N., Parker, K. H., Hughes, A. D., & Thom, S. A. M. (2019). Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content. PeerJ, 2019(6), 1–24. https://doi.org/10.7717/peerj.7119
Mohammed, A. A., Minhas, R., Wu, Q. M. J., & Sid-Ahmed, M. A. (2011). Human Face Recognition Based on Multidimensional PCA and Extreme Learning Machine. Pattern Recognition, 44(10–11), 2588–2597.
Muhammed, L. A. (2018). Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm. Int. J. Biomed. Imaging, 2018(13), 1–7.
Niemeijer, M., Abràmoff, M. D., & Ginneken, B. van. (2009). Fast Detection of The Optic Disc and Fovea in Color Fundus Photographs. Medical Image Analysis, 13(6), 859–870.
Niemeijer, M., Abràmoff, M. D., & Ginneken, B. Van. (2007). Segmentation of The Optic Disc, Macula and Vascular Arch in Fundus Photographs. IEEE Transactions on Medical Imaging, 26(1), 116–127.
Nija, K. S., Anitha, V. S., & Gopi, V. P. (2019). An Automated method of Optic Disc Detection from Retinal Fundus Images. 2019 International Conference on Contemporary Computing and Informatics (IC3I), 111–116.
Punithavathi, I. S. H., & Kumar, P. G. (2017). Severity Grading of Diabetic Retinopathy Using Extreme Learning Machine. 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 1–6.
Rahebi, J., & Hardalaç, F. (2016). A New Approach to Optic Disc Detection in Human Retinal Images using The Firefly Algorithm. Medical and Biological Engineering and Computing, 54(2–3), 453–461.
Rama, M. M. K., Acharya, U. R., Kuang, C., Min, C., Ng, E. Y. K., & Laude, A. (2013). Computer-aided diagnosis of diabetic retinopathy : A review. Computer in Biology and Medicine, 43, 2136–2155. https://doi.org/10.1016/j.compbiomed.2013.10.007
Ravishankar, S., Jain, A., & Mittal, A. (2009). Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 210–217.
Saraswathi, S., Sundaram, S., Sundararajan, N., Zimmermann, M., & Nilsen-Hamilton, M. (2011). ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins are Highly Represented. IEEE/ACM Trans. Comput. Biol. Bioinforma, 8(2), 452–463.
Sopharak, A., Nwe, K. T., Moe, Y. A., Dailey, M. N., & Uyyanonvara, B. (2008). Automatic Exudate Detection with a Naive Bayes Classifier. Proceeding of The 2008 International Conference on Embedded Systems and Intelligent Technology, 139–142.
Staal, J. J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & Ginneken, B. van. (2004). Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23, 501–509.
Tobin, K. W., Chaum, E., Govindasamy, V. P., & Karnowski, T. P. (2007). Detection of Anatomic Structures in Human Retinal Imagery. IEEE Trans. Med. Imaging, 26(12), 1729–1739.
Verma, L., Prakash, G., & Tewari, H. K. (2002). Diabetic retinopathy: time for action. No complacency please! Organ, 80(5), 419–420.
Wiharto, W., Suryani, E., & Kipti, M. Y. (2018). Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image. Telkomnika, 16(1), 445–454.
Wild, S., Roglic, G., Green, A., R., S., & Hilary, K. (2004). Global Prevalence of Diabetes: Estimates for the year 2000 and projection for 2030. Diabetes Care, 27(5), 1047–1053.
Xiong, L., & Li, H. (2016). An Approach to Locate Optic Disc in Retinal Images with Pathological Changes. Computerized Medical Imaging and Graphic, 47, 40–50.
Youssif, A. A. H. A. R., Ghalwash, A. Z., & Ghoneim, A. A. S. A. R. (2008). Optic Disc Detection from Normalized Digital Fundus Images by Means of A Vessels’ Direction Matched Filter. IEEE Trans. Med. Imaging, 27(1), 11–18.
Zheng, S., Pan, L., Chen, J., & Yu, L. (2014). Automatic and Efficient Detection of The Fovea Center in Retinal Images. Proceeding of International Conference on Biomedical Engineering and Informatics, BMEI 2014, 145–150.
Zheng, W., Qian, Y., & Lu, H. (2013). Text Categorization Based on Regularization Extreme Learning Machine. Neural Computing and. Application, 22(3–4), 447–456.
Zhou, W., Qiao, S., Yi, Y., Han, N., Chen, Y., & Lei, G. (2020). Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine. International Journal of Machine Learning and Cybernetics, 11, 55–69.
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Copyright (c) 2023 Sutikno, Helmie Arif Wibawa, Priyo Sidik Sasongko
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