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Segmentation and Detection of Diabetic Retinopathy Exudates
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 91 - Number 16 |
| Year of Publication: 2014 |
| Authors: A. Elbalaoui, M. Fakir, A. Merbouha |
10.5120/15963-5155
|
A. Elbalaoui, M. Fakir, A. Merbouha . Segmentation and Detection of Diabetic Retinopathy Exudates. International Journal of Computer Applications. 91, 16 ( April 2014), 7-13. DOI=10.5120/15963-5155
Abstract
Diabetic retinopathy, the most common diabetic eye disease, occurs when blood vessels in the retina change. Sometimes these vessels swell and leak fluid or even close off completely. In other cases, abnormal new blood vessels grow on the surface of the retina. Early detection can potentially reduce the risk of blindness. This paper presents an automated method for the detection of exudates in retinal color fundus images with high accuracy, First, the image is converted to HSI model, after preprocessing possible regions containing exudate, the segmented image without Optic Disc (OD) using algorithm Graph cuts, Invariant moments Hu in extraction feature vector are then classified as exudates and non-exudates using a Neural Network Classifier. All tests are applied on database DIARETDB1.
References
- K. V. Kauppi T, Kämäräinen J-K, Lensu L, Sorri I, Raninen A, "DIARETDB1: diabetic retinopathy database and evaluation protocol. In: Medical image understanding and analysis (MIUA). ",ed, 2007.
- Gardner GG, Keating D, Williamson TH, Elliott AT, "Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool", British journal of Ophthalmology, pp. 940-944, 1996.
- Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, "Automated detection of diabetic retinopathy on digital fundus image", Journal of Diabetic Medicine, vol 19, pp 105-112,2002.
- C. I. Sanchez, R. Hornero, M. I. Lopez, and J. Poza, Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy,IEEE Conf. on Engineering in Medicine and Biology Society, vol. 1, pp. 1624-1627, 2004.
- Kavitha, D. and S. D. Shenbaga. Automatic detection of optic disc and exudates in retinal images. Proceedings of the International Conference on Intelligent Sensing and Information Processing, Jan. 4-7, IEEE Xplore Press, pp: 502-506. 2005.
- AkaraSopharak, BunyaritUyyanonvara, Sarah Barman, "Automatic Exudate Detection from Non-dilated Diabetic Retinopathy retinal images using Fuzzy C-Means Clusterg" Journal of Sensors, vol. 9, No. 3, pp 2148- 2161, March 2009.
- D. Welfer, J. Scharcanski, D. R. Marinho, "A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images," Computerized Medical Imaging and Graphics, vol. 34, pp. 228-235, 2010.
- AkaraSopharak, Mathew N. Dailey, BunyaritUyyanonvara, Sarah Barman, Tom Williamson,Yin Aye Moe, "Machine Learning approach to automatic Exudates detection in retinal images from diabetic patients", Journal of Modern optics,Vol. 57, No. 2, pp. 124-135,Nov 2011.
- DeepashreeDevaraj, Dr. Prasanna KumarS. C. ,Manisha, "Automatic Exudate detection for the diagnosis of Diabetic Retinopathy",International Journal of Innovative Research and Studies,Volume 2 Issue 5- May 2013,Page No 657-669.
- S. F. Barrett, E. Naess, T. Molvik. , "Employing the hough transform to locate the optic disk", Biomedical Sciences Instrumentation 37 (2001) 81–86.
- Boykov, Y. , Veksler, O. , and Zabih, R. Fast approximate energy minimi-zation via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 11 (2001), 1222{1239.
- Boykov, Y. , and Jolly, M. -P. Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In International Conference on Computer Vision (ICCV) (2001), pp. 105{112}
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