Automated Espial of Microaneurysm and Grading of Diabetic Retinopathy in Fundus Images Using Ensemble-Based System

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-23 Number-1
Year of Publication : 2015
Authors : Shilpa K, V J Nyamati, Usha Rani G M
  10.14445/22312803/IJCTT-V23P103

MLA

Shilpa K, V J Nyamati, Usha Rani G M "Automated Espial of Microaneurysm and Grading of Diabetic Retinopathy in Fundus Images Using Ensemble-Based System". International Journal of Computer Trends and Technology (IJCTT) V23(1):12-15, May 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Diabetic retinopathy (DR) is a common complexity of diabetes that harms the retina and leads to vision loss if handled lately. DR can be diagnosed by earliest detection of microaneurysm (MA). Some methods have been developed, the accurate espial of MA in color retinal images is still a challenging task. In this paper, we propose a Ensemble-Based system to detect MA. We developed a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors and measured our approach for microaneurysm detection in an online competition, where this system is currently ranked as first.

References
[1]. Balint Antal, and Andras Hajdu, ―An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO. 6, JUNE 2012.
[2]. Von Wendt, G. Screening for diabetic retinopathy: Aspects of photographic methods. PhD thesis, Karolinska Institutet, November 2005.
[3]. Abramoff, M. Niemeijer, M. Suttorp-Schulten, M. A. Viergever, S. R. Russel, and B. van Ginneken, ―Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes, Diabetes Care, vol. 31, pp. 193–198, 2008.
[4]. D. Fleming, K. A. Goatman, S. Philip, G. J. Prescott, P. F. Sharp, and J. A. Olson, ―Automated grading for diabetic retinopathy: A large-scale audit using arbitration by clinical experts, Br. J. Ophthalmol., vol. 94, no. 12, pp. 1606–1610, 2010.
[5]. H. J. Jelinek, M. J. Cree, D. Worsley, A. Luckie, and P. Nixon, ―An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice, Clin. Exp. Optom., vol. 89, no. 5, pp. 299–305, 2006.
[6]. S.Vijayalakshmi, P.Sivaprakasam, ―Early Proliferation Stage of Detecting Diabetic Retinopathy Using Bayesian Classifier Based Level Set Segmentation, International Journal of Computer Trends and Technology (IJCTT) – volume 7 number 1– Jan 2014.
[7]. M. Abramoff, J. Reinhardt, S. Russell, J. Folk, V. Mahajan, M. Niemeijer, and G. Quellec, ―Automated early detection of diabetic retinopathy, Ophthalmology, vol. 117, no. 6, pp. 1147–1154, 2010.
[8]. Effective Health Care—Complications of Diabetes: Screening for Retinopathy and Management of Foot Ulcers,RoyalSocietyofMedicinePress5(4)(1999), ISSN 0965-0288.
[9]. K. Ram, G.D. Joshi, J. Sivaswamy, A successive clutter-rejectionbased approach for early detection of diabetic retinopathy, IEEE Transactions on Biomedical Engineering 58 (3) (2011) 664–673.
[10]. G. Quellec, M. Lamard, P.M. Josselin, G. Cazuguel, B. Cochener, C. Roux, Optimal wavelet transform for the detection of microaneurysms in retina photographs, IEEE Transactions on Medical Imaging 27 (9) (2008) 1230–1241.
[11]. A.D. Fleming, S. Philip, K.A. Goatman, J.A. Olson, P.F. Sharp, Automated microaneurysm detection using local contrast normalization and local vessel detection, IEEE Transactions on Medical Imaging 25 (9) (2006) 1223–1232.
[12]. T. Walter and J. Klein, ―Automatic detection of microaneurysm in colorfundus images of the human retina by means of the bounding box closing,Lecture Notes in Computer Science, vol. 2526. Berlin,Germany: Springer-Verlag, 2002, pp. 210–220.
[13]. K. Zuiderveld, ―Contrast limited adaptive histogram equalization,Graphics Gems, vol. 4, pp. 474–485, 1994.
[14]. S. Ravishankar, A. Jain, and A. Mittal, ―Automated feature extraction forearly detection of diabetic retinopathy in fundus images, in Proc. IEEEConf. Comput. Vision Pattern Recog., 2009, pp. 210–217.
[15]. A. A. A. Youssif, A. Z. Ghalwash, and A. S. Ghoneim, ―Comparativestudy of contrast enhancement and illumination equalization methods forretinal vasculature segmentation, in Proc. Cairo Int. Biomed. Eng. Conf.,2006, pp. 21–24.
[16]. T. Walter, P. Massin, A. Arginay, R. Ordonez, C. Jeulin, and J. C. Klein,―Automatic detection of microaneurysms in color fundus images, Med.Image Anal., vol. 11, pp. 555–566, 2007.
[17]. T. Spencer, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester,―An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus, Comput.Biomed.Res., vol. 29, pp. 284–302, 1996.
[18]. S. Abdelazeem, ―Microaneurysm detection using vessels removal and circularhough transform, in Proc. 19th National Radio Sci. Conf., pp. 421–426, 2002.
[19]. B. Zhang, X. Wu, J. You, Q. Li, and F. Karray, ―Detection of microaneurysmsusing multi-scale correlation coefficients, Pattern Recogn.,vol. 43, no. 6, pp. 2237–2248, 2010.
[20]. I. Lazar and A. Hajdu, ―Microaneurysm detection in retinal images usinga rotating cross-section based model, in Proc. IEEE Int. Symp. Biomed.Imag., 2011, pp. 1405–1409.
[21]. D. Chakraborty, ―Clinical relevance of the ROC and free-response paradigms for comparing imaging system efficacies, Radiation Protection Dosimetry, vol. 139, no. 1–3, pp. 37–41, 2010.
[22]. J. Eng. Roc analysis:Web-based calculator for roc curves. Johns Hopkins University, Baltimore. (2006). [Online]. Available: http://www.jrocfit.org

Keywords
Diabetic Retinopathy, Microaneurysm, Ensemble-based system, Preprocessing, Candidate Extractors.