The week's pick
Random Articles
Reseach Article
Reduction of False Microaneurysms in Retinal Fundus Images using Fuzzy C-Means Clustering in terms NLM Anisotropic Filter
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 113 - Number 19 |
| Year of Publication: 2015 |
| Authors: Ganesh Naga Sai Prasad.v, Habibulla Khan, E.gopinathan |
10.5120/19934-1911
|
Ganesh Naga Sai Prasad.v, Habibulla Khan, E.gopinathan . Reduction of False Microaneurysms in Retinal Fundus Images using Fuzzy C-Means Clustering in terms NLM Anisotropic Filter. International Journal of Computer Applications. 113, 19 ( March 2015), 20-25. DOI=10.5120/19934-1911
Abstract
The identification of MAs is an important phase in the research and grading of suffering from diabetes retinopathy. We present clustering strategy to identify the microaneurysms from the optic disk and cup in the retinal fundus pictures. Fuzzy C-Means (FCM) Clustering is used for clustering the information in which the information factors are grouped with different account level. The first and major phase is preprocessing function, in which the optic cup and disk of the feedback picture is being turned. Originally the optic hard disk is turned in some position and the range between the information factors is calculated and a group is established in accordance with the centroid. For retrieving micro aneurysms in all retinal images in our previous work we used SVM Classification filter in Fuzzy C-Means Clustering. In this paper we propose an effective filtering technique for micro aneurysms detection in retinal image preprocessing. Instead of SVM Filtering technique we used NLM in terms of Anisotropic Filter to process retinal images. Tested on the various simulated retina data repositories combining rotation and scaling, the developed method presents good results and shows robustness to rotations and scale changes.
References
- Early Treatment Diabetic Retinopathy Study Research Group, "Early photocoagulation for diabetic retinopathy," Ophthalmology, vol. 98, pp. 766–85, 1991.
- B. Lay, "Analyse automatique des images angiofluorographiques au cours de la retinopathie diabetique," Ph. D. dissertation, Centre of Mathematical Morphology, Paris School of Mines, Paris, France, 1983.
- "A Modified Spatial Fuzzy C-Means Clustering Algorithm For Detecting Glaucomain Retinal Fundus Images", by S. J. Grace Shoba and A. Brintha Therese, Journal of Computer Science 10 (8): 1362-1372, 2014 ISSN: 1549-3636 © 2014 Science Publications doi:10. 3844/jcssp. 2014. 1362. 1372 Published Online 10 (8) 2014 (http://www. thescipub. com/jcs. toc). .
- C. E. Baudoin, B. J. Lay, and J. C. Klein, "Automatic detection of microaneurysms in diabetic fluorescein angiographies," Rev. Epidemiol Sante Publique, vol. 32, pp. 254–261, 1984.
- "Retinal Microaneurysm Detection through Local Rotating Cross-Section Profile Analysis", Istvan Lazar* and Andras Hajdu, IEEE Transactions On Medical Imaging, VOL. 32, NO. 2, FEBRUARY 2013.
- G. Quellec, M. Lamard, P. Josselin, G. Cazuguel, B. Cochener, and C. Roux, "Optimal wavelet convert for the recognition ofmicroaneurysms in retina pictures," IEEE Trans. Med. Imag. , vol. 27, no. 9, pp. 1230–1241, Sep. 2008.
- C. I. Sanchez, R. Hornero, A. Mayonnaise, and M. Garcia, "Mixture model based clustering and logistic regression for automated recognition of microaneurysms in retinal pictures," in Proc. SPIE Med. Imag. 2009: Comput. - Assisted Analysis, 2009, vol. 72601M.
- I. Lazar and A. Hajdu, "Microaneurysm recognition in retinal images using a spinning cross-section centered design," in IEEE Int. Symp. Biomed. Imag. : From New ipod nano to Macro, 2011, pp. 1405–1409.
- J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, "Ridge centered boat segmentation in shade pictures of the retina," IEEE Trans. Med. Imag. , vol. 23, no. 4, pp. 501–509, Apr. 2004.
- T. Zhu, "Fourier cross-sectional information for boat recognition on retinal images," Comput. Med. Imag. Grap. , vol. 34, pp. 203–212, 2010.
- E. Ricci and R. Perfetti, "Retinal vein segmentation using line operators and assistance vector category," IEEE Trans. Med. Imag. , vol. 26, no. 10, pp. 1357–1365, Oct. 2007.
- L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin, and E. Chaum, "Microaneurysm recognition with radon transform-based classification on retina pictures," in Proc. IEEE Annu. Int. Conf. EMBC, 2011, pp. 5939–5942.
- M. Niemeijer, B. van Ginneken, M. Cree, A. Mizutani, G. Quellec, C. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray,M. Garcia, H. Fujita, andM. Abramoff, "Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs," IEEE Trans. Med. Imag. , vol. 29, no. 1, pp. 185–195, Jan. 2010.
- B. Antal and A. Hajdu, "An ensemble-based system for microaneurysm detection and diabetic retinopathy grading," IEEE Trans. Biomed. Eng. , vol. 59, no. 6, pp. 1720–1726, Jun. 2012.
- K. Ram, G. D. Joshi, and J. Sivaswamy, "A successive clutter-rejection- based approach for early detection of diabetic retinopathy," IEEE Trans. Biomed. Eng. , vol. 58, no. 3, pp. 664–673, Mar. 2011.
- B. Zhang, X. Wu, J. You, Q. Li, and F. Karray, "Detection of microaneurysms using multi-scale correlation coefficients," Pattern Recognit. , vol. 43, no. 6, pp. 2237–2248, 2010.
- "Identifying Microanyrisms in Retinal Images Using Fuzzy C-Means Clustering"by Ganesh, , IEEE Transactions On Medical Imaging, VOL. 32, NO. 2, 2013.
- http://www. ncbi. nlm. nih. gov/pmc/articles/PMC3758647.
Index Terms
Keywords