CFP last date
20 January 2025
Reseach Article

Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema

by Vipin Krishnan C V, V. S. Jayanthi, Jestin V. Kunjummen
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 8
Year of Publication: 2013
Authors: Vipin Krishnan C V, V. S. Jayanthi, Jestin V. Kunjummen
10.5120/11415-6748

Vipin Krishnan C V, V. S. Jayanthi, Jestin V. Kunjummen . Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema. International Journal of Computer Applications. 67, 8 ( April 2013), 22-26. DOI=10.5120/11415-6748

@article{ 10.5120/11415-6748,
author = { Vipin Krishnan C V, V. S. Jayanthi, Jestin V. Kunjummen },
title = { Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 8 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number8/11415-6748/ },
doi = { 10.5120/11415-6748 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:24:08.290776+05:30
%A Vipin Krishnan C V
%A V. S. Jayanthi
%A Jestin V. Kunjummen
%T Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 8
%P 22-26
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swelling in the macular region of retina which is also known as macular edema, is a complication of the eye often leading to reduced capacity of vision. Diabetic retinopathy is also a severe complication to vision. In this work, iterative kernel based PCA is proposed which is a novel method used for the classification purpose in diseased retinal images. Exudate detection is carried out via a supervised learning approach using the normal fundus images. Feature extraction is introduced to capture the global characteristics of the fundus images and discriminate the normal from diseased images. The performance of the proposed methodology with the conventional PCA is evaluated based on classification accuracy. Experimental results shows the superior nature of iterative kernel based PCA in terms of performance measures.

References
  1. Nathan silberman ,Lakshminarayanan Subramanian " case for automated detection of diabetic retinopathy"advancement of Artificial Intelligence 2010
  2. K. Sai Deepak and Jayanthi Sivaswamy, Member IEEE "Automatic Assessment of Macular Edema From Color Retinal Images" , IEEE Transaction on medical imaging,VOL. 31, NO. 3 march 2012
  3. C. P. Wilkinson, F. L. Ferris, R. E. Klein, P. P. Lee, C. D. Agardh, M. Davis, D. Dills, A. Kampik, R. Pararajasegaram, and J. T. Verdaguer,"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales," Am. Acad. Ophthalmol. , vol. 110,no. 9, pp. 1677–1682, Sep. 2003.
  4. A. Rocha, T. Carvalho, S. Goldenstein, and J. Wainer, Points of interest and visual dictionary for retina pathology detection Inst. Comput. , Univ. Campinas, Tech. Rep. IC-11-07, Mar. 2011.
  5. K. Ram and J. Sivaswamy, "Multi-space clustering for segmentation of exudates in retinal color photographs," in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. , Sep. 2009, pp. 1437–1440
  6. K. S. Deepak, G. D. Joshi, and J. Sivaswamy, "Content-based retrieval of retinal images for maculopathy," in Proc. 1st ACM Int. Health Inf. Symp. , 2010, pp. 135–143
  7. R. F. N. Silberman, K. Ahlrich, and L. Subramanian, "Case for automated detection of diabetic retinopathy," Proc. AAAI Artif. Intell. Development (AI-D'10), pp. 85–90, Mar. 2010.
  8. P. M. Narendra, and K. Fukunaga, "A branch and bound algorithm for feature selection," IEEE Transactions on Computers, 26(9), pp. 917-922 1977.
  9. M. Verma, R. Raman, and R. E. Mohan, "Application of tele ophthalmologyin remote diagnosis and management of adnexal and orbital diseases," Indian J. Ophthalmol. , vol. 57, no. 5, pp. 381–384, Jul. 2009
  10. C. Sanchez,A. Mayo,M. Lopez 'Automatic image processing algorithm to detect hard exudates based on mixturemodels'in. proc. 28thannu. inf. conf. IEEE. Eng. Med. Biol. sep 2006
  11. J. Davidson,T. Ciulla 'How the diabetic eye loss the vision' Endocrine vol. 32 Nov 2007
  12. C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis,S. Russell, M. Abramoff, and P. Soliz, "Multiscale am-fm methods for diabetic retinopathy lesion detection," IEEE Trans. Med. Imag. , vol. 29, no. 2, pp. 502–512, Feb. 2010.
  13. M. Kudo and J. Sklansky. "Comparison of Algorithms that select features for pattern classifiers," Pattern Recognition,33, pp. 25-41, 2000.
  14. K. S. Deepak, G. D. Joshi, and J. Sivaswamy, "Content-based retrieval of retinal images for maculopathy," in Proc. 1st ACM Int. Health Inf. Symp. , 2010, pp. 135–143
  15. Meindert Niemeijer, Micheal D. Abramoff, Member, IEEE,and Bram van Ginneken, Member, IEEE. "Segmentation of the optic disc,macula and vascular arch in fundus photographs",IEEE Transactions on Medical imaging,Vol. 26,No. 1,pp. 116-127,January 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetic Macular Edema Fundus images Hard exudates Iterative kernel based Principal Component Analysis Retinal images