* Corresponding Author
Hide
Show all
Deteksi Dini Retinopati Diabetik dengan Pengolahan Citra Berbasis Morfologi Matematika
https://doi.org/10.22146/ijccs.24761 Lukman Heryawan(1*)
(1) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Diabetic retinopathy is a complication caused by diabetes mellitus. Diabetic retinopathy, if not handled properly can lead to blindness. A necessary step to prevent blindness is early detection. Early detection can be done by finding the initial symptoms that microaneurysm. In this research, a system made to detect diabetic retinopathy using algorithms detection microaneurysm with mathematical morphology. The algorithm is divided into three stages of preprocessing, detecting candidate microaneurysm and postprocessing. In this research, the system will be made by using a raspberry pi as the media. To see how well the system detects diabetic retinopathy, the test will be done. in the tests performed, system obtained an accuracy of 90%, sensitivity 90, and specificity of 55% using data diaretdb1. While testing using data from e-ophtha obtained results with an accuracy of 70.5%, a sensitivity of 80% and a specificity of 60%.
Keywords
Full Text:
PDFReferences
[1] R. Sitompul, “Retinopati Diabetik,” J Indon Med Assoc, vol. 61(8), no. Dm, pp. 337–341, 2011.
[2] K. Noronha and K. P. Nayak, “A Review of Fundus Image Analysis for the Automated Detection of Diabetic Retinopathy,” J. Med. Imaging Heal. Informatics, vol. 2, no. 3, pp. 258–265, 2012.
[3] N. Singh and R. C. Tripathi, “Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques,” Int. J. Comput. Appl., vol. 8, no. 2, pp. 18–23, 2010.
[4] R. Y. Dillak and A. Harjoko, “Klasifikasi Fase Retinopati Diabetes Menggunakan Backpropagation Neural Network,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 7, no. 1, pp. 23–34, 2013 [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/3049. [Accessed: 04-May-2017]
[5] A. Purwita and K. Adityowibowo, “Automated Microaneurysm Detection using Mathematical Morphology,” Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng., no. November, pp. 1-4, 2011.
[6] R. Vidyasari, I. Sovani, T. L. R. Mengko, and H. Zakaria, “Vessel enhancement algorithm in digital retinal fundus microaneurysms filter for nonproliferative diabetic retinopathy classification,” Proc. - Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng. 2011, ICICI-BME 2011, no. November, pp. 278–281, 2011.
[7] M. N. Langroudi and H. Sadjedi, “A new method for automatic detection and diagnosis of retinopathy diseases in colour fundus images based on Morphology,” Bioinforma. Biomed. Technol. (ICBBT), 2010 Int. Conf., pp. 134–138, 2010.
[8] G. B. Kande, T. S. Savithri, and P. V. Subbaiah, “Automatic detection of microaneurysms and hemorrhages in digital fundus images,” J. Digit. Imaging, vol. 23, no. 4, pp. 430–437, 2010.
[9] R. Haldar, S. Aruchamy, A. Chatterjee, and P. Bhattacharjee, “Diabetic Retinopathy Image Enhancement using Vessel Extraction in Retinal Fundus Images by programming in Raspberry Pi Controller Board,” pp. 37–42, 2016.
[10] N. Karunanayake, M. Gnanasekera, and N. D. Kodikara, “An Improved Method for Optic Disc Localization,” Int. J. Comput. Appl., vol. 128, no. 13, pp. 975–8887, 2015.
DOI: https://doi.org/10.22146/ijccs.24761
Article Metrics
Abstract views : 4939
| 
views : 4502
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1