Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2016, 4(3), 59-66
DOI: 10.12691/jcsa-4-3-2
Open AccessArticle

Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors

Kemal AKYOL1, , Şafak BAYIR1, Baha ŞEN2 and Hasan B. ÇAKMAK3

1Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey

2Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, Turkey

3Department of Ophthalmology, Faculty of Medicine, Hacettepe University, Turkey

Pub. Date: November 29, 2016

Cite this paper:
Kemal AKYOL, Şafak BAYIR, Baha ŞEN and Hasan B. ÇAKMAK. Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors. Journal of Computer Sciences and Applications. 2016; 4(3):59-66. doi: 10.12691/jcsa-4-3-2


Diabetic retinopathy is one of the main complications of diabetes mellitus and it is a progressive ocular disease, the most significant factor contributing to blindness in the later stages of the disease. It has been a subject of many studies in the medical image processing field for a long time. Hard exudates are one of the primary signs of early stage diabetic retinopathy diagnosis. Immediately identifying hard exudates is of great importance for the blindness and coexistent retinal edema. There are various ways of achieving meaningful information from an image and one of them is key point extraction method. In this study, we presented a technique based on the acquisition of important information by utilizing the description information about the image within the framework of the learning approach in order to identify hard exudates. This technique includes the learning and testing processes of the system in order to make the right decisions in the analysis of new retinal fundus images. We performed experimental validation on DIARETDB1 dataset. The obtained results showed us the positive effects of machine learning technique suggested by us for the detection of hard exudates.

biomedical image processing feature extraction image classification image recognition machine learning

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[1]  R. Singh, K. Ramasamy, C. Abraham, V. Gupta, A. Gupta, “Diabetic retinopathy: An update,” Indian J Ophthalmol, vol. 56, no. 3, pp. 179-188, 2008.
[2]  J. A. Olson, F. M. Strachan, J. H. Hipwell, K. A. Goatman, K. C. McHardy, J. V. Forrester, P. F. Sharp, “A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy,” Diabet Med, vol. 20, no. 7, pp. 528-534, 2003.
[3]  Q. Mohamed, M. C. Gillies, T. Y. Wong, “Management of diabetic retinopathy: A systematic review,” JAMA, vol. 298, no. 8, pp. 902-916, 2007.
[4]  R. Venkatesan, P. Chandakkar, B. Li, H. K. Li, “Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features,” in Conf Proc IEEE Eng Med Biol Soc, San Diego, pp. 1462-1465, 2012.
[5]  A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, “Automated identification of diabetic retinal exudates in digital color images,” Br J Ophthalmol, vol. 87, no. 10, pp. 1220-1223, 2003.
[6]  S. A. Naqvi, M. F. Zafar, I. U. Haq, “Referral system for hard exudates in eye fundus,” Comput Biol Med, vol. 64, pp. 217-235, 2015.
[7]  T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Kalviainen, J. Pietila, “Diaretdb1 diabetic retinopathy database and evaluation protocol,” in Proc. of the British Machine Vision Conference, University of Warwick, UK, pp. 252-261, 2007.
[8]  K. Mikolajczyk, C. Schmid, “A performance evaluation of local descriptors,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2005.
[9]  Y. G. Jiang, C. W. Ngo, J. Yang, “Towards optimal bag-of-features for object categorization and semantic video retrieval,” in Proc. of the 6th ACM International Conference on Image and Video Retrieval, New York, 2007, pp. 494-501.
[10]  S. Lazebnik, C. Schmid, J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in Proc. of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 2169-2178.
[11]  F. F. Li, P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 524-531.
[12]  J. Sivic, A. Zisserman, “Video google: A text retrieval approach to object matching in videos,” in Proc. of 9th IEEE International Conference on Computer Vision, Nice, 2003, pp. 1470-1477.
[13]  H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, “SURF: Speeded-up robust features,” Comput Vis Image Und vol. 110, no. 3, pp. 346-359. 2008.
[14]  E. Rublee, V. Rabaud, K. Konolige, G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” in IEEE International Conference on Computer Vision (ICCV), pp. 2564-2571, 6-13 Nov 2011.
[15]  X. Chen, W. Bu, X. Wu, B. Dai, Y. Teng, “A novel method for automatic hard exudates detection in color retinal images,” in IEEE International Conference on Machine Learning and Cybernetics, pp. 1175-1181, Jul. 2012.
[16]  M. Garcia, C. Valverde, M. I. Lopez, J. Poza, R. Hornero, “Comparison of logistic regression and neural network classifiers in the detection of hard exudates in retinal images,” in IEEE International Conference on Engineering in Medicine and Biology Society, pp. 5891-5894, Jul. 2013.
[17]  D. Kayal, S. Banerjee, “A new dynamic thresholding based technique for detection of hard exudates in digital retinal fundus image,” in IEEE International Conference on Signal Processing and Integrated Networks, pp. 141-144, Feb. 2014.
[18]  N. G. Ranamuka, R. G. N. Meegama, “Detection of hard exudates from diabetic retinopathy images using fuzzy logic,” IET Image Process, vol. 7, no. 2, pp. 121-130, 2013.
[19]  C. I. Sanchez, M. Niemeijer, M. S. A. Suttorp Schulten, M. Abramoff, B. Van Ginneken, “Improving hard exudate detection in retinal images through a combination of local and contextual information,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, 2010, pp. 5-8.
[20]  H. Tjandrasa, R. E. Putra, A. Y. Wijaya, I. Arieshanti, “Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM,” in IEEE International Conference on Control System, Computing and Engineering, pp. 376-380, Dec. 2013.
[21]  L. Xu, S. Luo, “Support vector machine based method for identifying hard exudates in retinal images,” in IEEE Youth Conference on Information Computing and Telecommunication, pp. 138-141, Sept. 2009.
[22]  D. Kavitha, S. Shenbaga Devi, “Automatic detection of optic disc and exudates in retinal images,” in Proc. of 2005 International Conference on Intelligent Sensing and Information Processing, 2005, pp. 501-506.
[23]  C. JayaKumari, R. Maruthi, “Detection of hard exudates in color fundus images of the human retina,” Procedia Engineering, vol. 30, pp. 297-302, 2011.
[24]  A. Sopharak, B. Uyyanonvara, S. Barman, T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 720–727, 2008.
[25]  C. Pereira, L. Gonçalves, M. Ferreira, “Exudate segmentation in fundus images using an ant colony optimization approach,” Information Sciences, vol. 296, pp. 14-24, 2015.
[26]  I. Guyon, J. Weston, S. Barnhill, V. Vapnik, “Gene selection for cancer classification using support vector machines,” Mach Learn, vol. 46, no. 1-3, pp. 389-422, 2002.
[27]  A. Bosch, A. Zisserman, X. Muoz, “Image classification using random forests and ferns,” in IEEE 11th International Conference on Computer Vision, pp. 1-8, Oct. 2007.
[28]  J. Li, J. Cheng, J. Shi, F. Huang, “Brief introduction of back propagation (BP) neural network algorithm and its improvement,” Advances in Computer Science and Information Engineering, vol. 169 of the series Advances in Intelligent and Soft Computing, pp. 553-558, 2012.
[29]  “Diabetic retinopathy database and evaluation protocol (DIARETDB1),” Electronic material (Online).
[30]  P. Eusebi, “Diagnostic Accuracy Measures,” Cerebrovasc Dis, vol. 36, no. 4, pp. 267-272, 2013.