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Automated Image Processing to Diagnose Exudates from Images of Retina
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 5 |
| Year of Publication: 2015 |
| Authors: Sanjeevani Choudhary, Jyotika Pruthi |
10.5120/20150-2286
|
Sanjeevani Choudhary, Jyotika Pruthi . Automated Image Processing to Diagnose Exudates from Images of Retina. International Journal of Computer Applications. 115, 5 ( April 2015), 41-44. DOI=10.5120/20150-2286
Abstract
Diabetic Retinopathy (DR) is a significant reason for blindness. Exudates are one of the essential indications of diabetic retinopathy which is a fundamental driver of visual weakening that could be counteracted with an early screening procedure. In this approach, the procedure and learning of automated image processing to diagnose exudates from image set of retina are implemented. The segmented image along with Optic Disk (OD) is selected. To Classify these segmented area, features considered as color base and texture are removed. The chosen feature vector is then grouped into exudates and non-exudates utilizing a Support Vector Machine (SVM) Classifier. An automated strategy to distinguish and restrict the vicinity of exudates from low-contrast digital images of Retinopathy patient's with non- dilated pupils is proposed.
References
- Olson. J. A, Strachana. F. M, Hipwell. J. H, "A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy" Journal on Diabetes Med. Vol. 20, No. 7 . pp. 528- 534, July 2003.
- Alireza Osareh, Bita shadgar and Richard Markham,"A computational intelligence based approach for detection of exudates in Diabetic Retinopathy Images", IEEE Trans. onInformation Technology in Biomedicines, vol. 13, no. 4, pp. 535-545, July 2009.
- Saiprasad Ravishankar, Arpit Jain, Anurag Mittal, "Automated feature extraction for early detection of Diabetic Retinopathy in fundus images". IEEE Conference on Computer vision andpattern Recognition, pp. 210-217, August 2009.
- Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, "Automatic Exudate Detection from Non-dilated Diabetic Retinopathy retinal images using Fuzzy C-Means Clustering" Journal ofSensors, vol. 9, No. 3, pp 2148- 2161, March 2009.
- Niemeijer. B. V, Ginnekan. S. R, Russell. M, and M. D. Abramoff, "Automated detection and differentiation of drusen, exudates and cotton- wool spots in digital color fundus photographs for diabetic retinopathy diagnosis", Journal on Investigate Ophthalmol. And Visual Science. vol. 48, No. 2 pp. 2260-2267, 2007.
- Akara Sopharak, Mathew N. Dailey, Bunyarit Uyyanonvara, 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.
- T. Walter, J. Klein, P. Massin and A. Erginary, "A Contribution of image processing to the diagnosis of Diabetic Retinopathy detection of exudates in color fundus images of the human retina", IEEE Trans. On Med. images, vol. 21, no. 10, pp. 1236-1243, 2002.
- C. Sinthanayothin, "Image analysis for automatic diagnosis of Diabetic Retinopathy", Journalof Medical Science, Vol. 35,No. 5, pp. 1491-1501, Jan 2011.
- Fleming. AD, Philips. S, Goatman. KA, Williams. GJ, Olson. JA, sharp. PF, "Automated detection of exudates for Diabetic Retinopathy Screening", Journal of Phys. Med. Bio. , vol. 52, no. 24, pp. 7385-7396, 2007.
- Seongijin Park, Bohyoung Kim, Jeongjin Loe "GGO nodule volume preserving Non-rigid Lung Registration using GLCM texture analysis", IEEE Trans. On Biomedical Engg. , vol. 58, no. 10, pp. 2885-2894, sept 2011
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