Research Article
Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 111 - Issue 16 |
| Published: February 2015 |
| Authors: P.Latha, R.Vijayalakshmi |
10.5120/19623-1497
|
P.Latha, R.Vijayalakshmi . Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image. International Journal of Computer Applications. 111, 16 (February 2015), 23-26. DOI=10.5120/19623-1497
@article{ 10.5120/19623-1497,
author = { P.Latha,R.Vijayalakshmi },
title = { Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 111 },
number = { 16 },
pages = { 23-26 },
doi = { 10.5120/19623-1497 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A P.Latha
%A R.Vijayalakshmi
%T Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image%T
%J International Journal of Computer Applications
%V 111
%N 16
%P 23-26
%R 10.5120/19623-1497
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Automated detection of retinal hemorrhages in fundus image[2] is crucial step towards early detection or screening is difficult among large population. A novel splat feature classification method is introduced to detect retinal hemorrhages. Classification is been achieved through supervised learning approaches. The performance of sensitivity and specificity is been improved while processing with retinal hemorrhages than with lesions. An area under receiver operating characteristics curve (ROC) of 0. 96 can be achieved at splat level and 0. 87 at image level.
References
- Li Tang, Meindert Niemeijer, Joseph M. Reinhardt, Mona K. Garvin, and Michael D. Abràmoff*, "Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images" IEEE Trans on Med img. Vol 32,pp. 364-375 Feb 2011.
- M. D. Abràmoff, J. M. Reinhardt, S. R. Russell, J. C. Folk, V. B. Mahajan, M. Niemeijer, and G. Quellec, "Automated early detection of diabetic retinopathy" Ophthalmology, no. 6, pp. 1147–1154, Apr. .
- M. Niemeijer, B. van Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abràmoff, "Automatic detection of red lesions in digital color fundus photographs," IEEE Trans. Med. Imag. , vol. 24, no. 5, pp. 584–592, May 2005.
- P. Jitpakdee, P. Aimmanee, and B. Uyyanonvara, "A survey on hemorrhage detection in diabetic retinopathy retinal images," in Proc. 9th Int. Conf. Elect. Eng. /Electron. , Comput. , Telecommun. Inf. Technol. (ECTI-CON), Bangkok, Thailand, 2012, pp. 1–4, vol. .
- Vijayalakshmi R and Selvarajan S, (2014) "Comparative Studies on Canny and Sobel Edge Detection Techniques for Diabetic Retinopathy Eye Image" International Journal of Soft Computing, Vol. 9, Issue. 4, pp. 213-218.
- M. Abràmoff, M. Garvin, and M. Sonka, "Retinal imaging and image analysis," IEEE Rev. Biomed. Eng. , vol. 3, pp. 169–208, 2010.
- X. Ren and J. Malik, "Learning a classification model for segmentation," in Int. Conf. Comput. Vis. , 2003, vol. 1, pp. 10–17.
- L. Tang, M. Niemeijer, and M. Abràmoff, "Splat feature classification: Detection of the presence of large retinal hemorrhages," in Proc. IEEE 8th Int. Symp. Biomed. Imag. (ISBI), 2011, pp. 681–684.
- R. Kohavi and G. John, "Wrappers for feature subset selection," Artif. Intell. , vol. 97, no. 1–2, pp. 272–324, 1997.
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