Analisis Pengaruh Kompresi Citra Fundus terhadap Kinerja Sistem Automated Microanerysm Detections

  • Anugerah Galang Persada Universitas Gadjah Mada
  • Ahmad Nasikun
  • Igi Ardiyanto
  • Hanung Adi Nugroho Universitas Gadjah Mada


Diabetes is one of the most serious diseases that commonly suffered by people around the world, including Indonesia. Early symptoms of diabetes could be observed from various indicators, one of which is through the retina. Retina conditions is affected by diabetics and when remain unproperly threated could lead to blindness. This retinal disorders due to diabetes is normally called Diabetic Retinopathy (DR). One method that able to distinguish and detect DR is microaneurysm detection. This method requires good quality of retinal images. However, in certain areas such as rural areas, this requirement may difficult to meet due to lack of adequate infrastructure. One solution that may overcome this problem is to compress the images. In this paper, image compression algorithms were applied to the retinal image, and then used to detect microaneuryms through Deep Learning-based systems. The result shows that the most stable and appropriate algorithm is PNG, which is able to correctly classify around 83% in accuracy with 5,5% variance.


[1] IDF, ―IDF Diabetes Atlas 2015,‖ International Diabetes Federation. p. 1, 2015.
[2] V. M. Mane and D. V. Jadhav, ―Progress towards automated early stage detection of diabetic retinopathy: Image analysis systems and potential,‖ Journal of Medical and Biological Engineering, vol. 34, no. 6. pp. 520–527, 2014.
[3] C. Diabetes Association Clinical Practice Guidelines Expert Committee, R. Goldenberg FRCPC FACE, and Z. Punthakee FRCPC, ―Clinical Practice Guidelines Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome Canadian Diabetes Association Clinical Practice Guidelines Expert Committee,‖ Can. J. Diabetes, vol. 37, pp. S8–S11, 2013.
[4] K. G. M. M. Alberti and P. Z. Zimmet, ―Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation,‖ Diabet. Med., vol. 15, no. 7, pp. 539–553, 1998.
[5] O. Faust, R. Acharya U., E. Y. K. Ng, K.-H. H. Ng, and J. S. Suri, ―Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review,‖ J. Med. Syst., vol. 36, no. 1, pp. 145–157, 2012.
[6] C. I. Sanchez et al., ―A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis.,‖ Med. Eng. Phys., vol. 30, no. 3, pp. 350–7, 2008.
[7] S. Kanth, A. Jaiswal, and M. Kakkar, ―Identification of different stages of Diabetic Retinopathy using artificial neural network,‖ 2013 Sixth Int. Conf. Contemp. Comput., pp. 479–484, 2013.
[8] G. B. Kande, P. V. Subbaiah, and T. S. Savithri, ―Feature extraction in digital fundus images,‖ J. Med. Biol. Eng., vol. 29, no. 3, pp. 122–130, 2009.
[9] K. S. S.karthick and A. Priyadharsini, ―A Survey on Hard Exudates Detection and Segmentation,‖ Int. J. Sci. Eng. Technol., vol. 3, no. 2, pp. 154–158, 2014.
[10] A. D. Fleming et al., ―The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.,‖ Br. J. Ophthalmol., vol. 94, no. 9, pp. 706–711, 2006.
[11] A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp, ―Automated microaneurysm detection using local contrast normalization and local vessel detection,‖ IEEE Trans. Med. Imaging, vol. 25, no. 9, pp. 1223–1232, 2006.
[12] S. Bhavani, ―A Survey On Coding Algorithms In Medical Image Compression,‖ Int. J., vol. 2, no. 5, pp. 1429–1434, 2010.
[13] M. F. Ukrit, A.Umamageswari, and G.R Suresh, ―A Survey on Lossless Compression for Medical Images,‖ Int. J. Comput. Appl., vol. 31, no. 8, pp. 47–50, 2011.
[14] B. Alagendran and S. Manimurugan, ―A Survey on Various Medical Image Compression Techniques,‖ Int. J. Soft Comput. Eng., vol. 2, no. 1, pp. 425–428, 2012.
[15] H. J. Jelinek, M. J. Cree, D. Worsley, A. Luckie, and P. Nixon, ―An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice,‖ Clin. Exp. Optom., vol. 89, no. 5, pp. 299–305, 2006.
[16] M. Rehman, M. Sharif, and M. Raza, ―Image compression: A survey,‖ Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 4, pp. 656–672, 2014.
[17] M. A. Hall and G. Holmes, ―Benchmarking Attribute Selection Techniques for Discrete Class Data Mining,‖ IEEE Trans. Knowl. Data Eng., vol. 15, no. 6, pp. 1437–1447, 2003.
[18] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, ―Image quality assessment: from error visibility to structural similarity.,‖ IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–12, Apr. 2004.
[19] J. Staal, M. D. Abràmoff, M. Niemeijer, M. a Viergever, and B. van Ginneken, ―Ridge-based vessel segmentation in color images of the retina.,‖ IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501–9, Apr. 2004.
[20] A. F. M. Hani, T. A. Soomro, and I. Fayee, ―Non-invasive contrast enhancement for retinal fundus imaging,‖ Proc. - 2013 IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2013, pp. 197–202, 2013.
How to Cite
Anugerah Galang Persada, Ahmad Nasikun, Igi Ardiyanto, & Hanung Adi Nugroho. (2018). Analisis Pengaruh Kompresi Citra Fundus terhadap Kinerja Sistem Automated Microanerysm Detections . Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 7(1), 72-78. Retrieved from