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Detection of Exudates for the Diagnosis of Diabetic Retinopathy

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Diabetes is a group of metabolic diseases in which a person has high blood sugar. Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body. Diabetic Retinopathy affects 80% of all patients who had diabetes for 10 years or more, which can also lead to vision loss. The most primitive sign of Diabetic Retinopathy is Exudates. Exudates in the retina are opacities that result from the escape of plasma and white blood cells from defective blood vessels. Detecting the exudates in an earlier stage can prevent the vision loss. In this paper, an automated algorithm has demonstrated to detect and localize the presence of exudates from low-contrast digital images of retinopathy patients with non-dilated pupils. In this method, first the retinal fundus image is pre-processed. Then, Mask Technique and Score Computation technique is used for segmenting the exudates in the retinal fundus images. This method does not require supervised learning which requires labeled set, may cause human error and it is time consuming process. It can effectively identify the lesions because exudates were clearly distinguished from optic disc and blood vessels. It helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it.
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  • 1
116-120
  • English

REFERENCES

References: 

[1] K. Sai Deepak, J. Sivaswamy, “Automatic Assessment of Macular Edema from Colour Retinal Images,” Medical Imaging,
IEEE Transactions, vol. 31, no. 3, pp. 766-776, March 2012.
[2] L. Giancardo, F. Meriaudeau, T. Karnowski, K. Tobin, E. Grisan, P. Favaro, A. Ruggeri, and E. Chaum, “Textureless macula
swelling detection with multiple retinal fundus images,” IEEE Trans. Biomed. Eng., vol. 58, no. 3, pp. 795–799, March
2011.
[3] K. Ram, G. D. Joshi, J. Sivaswamy, “A Successive Clutter-Rejection Based Approach for Early Detection of Diabetic
Retinopathy,” IEEE Transactions on Biomedical Engineering, Vol.58, no. 3, March 2011.
[4] C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell, M. Abramoff, and P. Soliz, “Multiscale AMFM
methods for diabetic retinopathy lesion detection,” Medical Imaging, IEEE Transactions, vol. 29, no. 2, pp. 502 –512,
Feb. 2010.
[5] K. Ram and J. Sivaswamy, “Multi-space Clustering for segmentation of Exudates in Retinal Colour Photographs”, 31st
Annual International Conference of the IEEE EMBS,USA, pp. 1437-1440, September 2009.
[6] Alireza Osare et al., “A Computational–Intelligence Based Approach for Detection of Exudates in Diabetic Retinopathy
Images”, IEEE Transactions on Information Technology in Biomedicine. Vol. 13. no. 4, pp. 535-545, July 2009.
[7] Akara Sopharek et al., “Automatic exudates detection for diabetic retinopathy Screening”, 10.2306/Science asia1513-
1874.2009. 35.080, pp. 80-88, Feb 2009.
[8] Akara sopharak, Matthew N. Dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom Williamson Khine thet New and Yin Aye
Moe, “Machine learning approach to automatic Exudates detection in retinal images from diabetics patients”, Journal of
Modern Optics, pp. 1-17, Jan. 2008.
[9] M. Niemeijer, Michael D. Abràmoff, Bram van Ginneken, “Segmentation of the Optic Disc, Macula and Vascular Arch in
Fundus Photographs,” IEEE Transactions On Medical Imaging, Vol. 26, no.1, Jan. 2007.

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