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An Optimal Microaneurysm Detection Technique in Digital Fundus Images for DR Recognition

P. Ananthi

Abstract


In this method, an optimal combination of the internal components of Microaneurysm detectors is used to detect microaneurysms and Diabetic Retinopathy grading is done based on the number of microaneurysms present. Diabetic Retinopathy is a serious eye disease which is caused due to Diabetes. The means of early recognition of diabetic retinopathy is to detect microaneurysms in the retinal fundus on time. Microaneurysms are small dark red spots that appear on the surface of the retina. In medical image processing, reliable detection of microaneurysms is still an open issue. In this method, several preprocessing methods and candidate extractors, which are the internal components of microaneurysm detectors are combined. This system ensures high flexibility by using a modular model and a simulated annealing-based search algorithm is used to find the optimal combination. Images from Messidor database which is publicly available is used for testing. DR grading is done based on the presence or absence of microaneurysms and the number of microaneurysms present.

Keywords


Diabetic Retinopathy (DR), Retinal Fundus Image Processing, Microaneurysm (MA) Detection, Ensemble-based Systems.

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References


B. Antal and A. Hajdu,” An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading” IEEE Trans on Biomedical Imaging, Vol. 59, No. 6, June 2012

T. Walter and J. Klein, “Automatic detection of microaneurysm in color fundus 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.

K. Zuiderveld, “Contrast limited adaptive histogram equalization,” Graphics Gems, vol. 4, pp. 474–485, 1994.

S. Ravishankar, A. Jain, and A. Mittal, “Automated feature extraction for early detection of diabetic retinopathy in fundus images,” in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2009, pp. 210–217.

A. Criminisi, P. Perez, and K. Toyama, “Object removal by exemplarbased inpainting,” in Proc. IEEE Conf. Comput. Vision Pattern Recog.,vol. 2, 2003, pp. II-721–II-728.

A. A. A. Youssif, A. Z. Ghalwash, and A. S. Ghoneim, “Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation,” in Proc. Cairo Int. Biomed. Eng. Conf.,2006, pp. 21–24.

Hoover. A and Goldbaum. M (2003), “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans.Med. Imag., vol. 22, no. 8, pp. 951-958.

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.

A. D. Fleming, S. Philip, and K. A. Goatman, “Automated microaneurysm detection using local contrast normalization and local vessel detection,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1223–1232, Sep. 2006.

S. Abdelazeem, “Microaneurysm detection using vessels removal and circular hough transform,” in Proc. 19th National Radio Sci. Conf., pp. 421– 426, 2002.

Sekineh Asadi Amiri, Hamid Hassanpour, Masoumeh Shahiri, Reza Ghaderi (2012), “Detection of Microaneurysms in Retinal Angiography Images using the Circular Hough Transform”, Journal of Avances in Computer Research, Vol. 3, No.1, pp 1-12.

B. Zhang, X. Wu, J. You, Q. Li, and F. Karray, “Detection of microaneurysms using multi-scale correlation coefficients,” Pattern Recogn.,vol. 43, no. 6, pp. 2237–2248, 2010.

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.

Kirkpatrick S., Gelatt C. D., and Vecchi M. P. (1983), “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680.

Antal. B, Lazar. I, Hajdu.A, Torok.Z, Csutak A., Peto.T (2011), “Evaluation of the grading performance of an ensemble-based microaneurysm detector”, In proc. of Engineering in Medicine and Biology Society, EMBC.

K. Adal, S. Ali, D. Sidib, T. Karnowski, E. Chaum, F. Meriaudeau (2013), “Automated Detection of Microaneurysms Using Robust Blob Descriptors, SPIE Medical Imaging - Computer-Aided Diagnosis.


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