Abstract
Diabetic retinopathy has become an increasingly important cause of blindness. Nevertheless, vision loss can be prevented from early detection of diabetic retinopathy and monitor with regular examination. Common automatic detection of retinal abnormalities is for microaneurysms, hemorrhages, hard exudates, and cotton wool spot. However, there is a worse case of retinal abnormality, but not much research was done to detect it. It is neovascularization where new blood vessels grow due to extensive lack of oxygen in the retinal capillaries. This paper shows that various combination of techniques such as image normalization, compactness classifier, morphology-based operator, Gaussian filtering, and thresholding techniques were used in developing of neovascularization detection. A function matrix box was added in order to classify the neovascularization from natural blood vessel. A region-based neovascularization classification was attempted as a diagnostic accuracy. The developed method was tested on images from different database sources with varying quality and image resolution. It shows that specificity and sensitivity results were 89.4% and 63.9%, respectively. The proposed approach yield encouraging results for future development.
This is a preview of subscription content, log in via an institution to check access.
References
Jaudin R: Report of screening for diabetic retinopathy. Ministry of Health Malaysia, Putrajaya, 2006
Javitt JC: Cost effectiveness of current approaches to the control of retinopathy in Type I diabetics. Ophthalmology 9:255–264, 1989
Walter T, Klein J-C, Massin P, Erginay A: A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy – Detection of Exudates in Color Fundus Images of the Human Retina. IEEE Trans Med Imaging 21:1236–1243, 2002
Sopharak A, Uyyanonvara B: Automatic exudates detection on Thai diabetic retinopathy patients’ retinal images. Proceedings of the 2006 ECTI International Conference, 2006, pp 709–712
Niemeijer M, Van Ginneken B, Staal J, Suttorp-Schulten MSA, Abramoff MD: Automatic Detection of Red Lesions in Digital Color Fundus Photographs. Med Imaging IEEE Trans 24(24):584–592, 2005
Shivaram JM, Patil R, Anarind HS: Automated Detection and Quantification of Hemorrhages in Diabetic Retinopathy Images Using Image Arithmetic and Morphological Methods. Int J Recent Trends Eng 2(6):174–176, 2009
Zhang L, Li Q, You J, Zhang D: A Modified Matched Filter with Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy. IEEE Trans Inf Technol Biomed 13(4):528–534, 2009
Takerkart K, Fenouil R, Piovano J, Reynaud A, Hoffart L, Chavane F, Papadopoulo T, Conrath J, Masson GS: A Quantification framework for post-lesion neovascularization in retinal angiography. Proceedings of the 5th IEEE Int. Sym. Biomed Imaging, 2008, pp 1457–1460
Usher D, Dumskyi M, Himaga M, Williamson TH, Nussey S, Boyce J: Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening. Diabet UK Diabet Med 21(1):84–90, 2004
Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ: Retinal image analysis: Concepts, applications, and potentials. Prog Retinal Eye Res 25:99–127, 2006
Rudnicka ARR, Birch J: Diabetic eye-disease-identification & co-management. Elsevier, Amsterdam, 2000
Kovesi P: Matlab and octave functions for computer vision and image processing. Retrieved March 8, 2009 from School of Computer Science and Software Engineering, The University of Western Australia. Available online: http://www.csse.uwa.edu.au
Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M: Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269, 1989
Hoover A, Kouznetsova V, Goldbaum M: Locating blood vessel in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210, 2000
DIARETDB1 database. Available online: http://www2.it.l ut.fi/project/imageret/diaretdb1/
DIARETDB0 database. Available online: http://www2.it.lut.fi/project/imageret/diaretdb0/index.html
MESSIDOR database. Available online: http://messidor.crihan.fr
DRIVE database. Available online: http://www.isi.uu.nl/Research/Databases/DRIVE/
STARE database. Available online:http://www.parl.clemson.edu/stare/probing/
Zweig MH, Campbell G: Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577, 1993
Sopharak A, Uyyanonvara B, Barman S, Williamson TH: Automatic Detection of Diabetic Retinopathy Exudates from non-dilated Retinal Images using Mathematical Morphology methods. Sci Direct Comput Med Imaging Graphics 32:720–727, 2008
Acknowledgment
The project was supported by the grants from the National Science Foundation of Malaysia. The authors would like to thank to local general hospital and all the public available databases (DRIVE, STARE, Diaretdb0, and Messidor program) for providing the diabetic retinal images and clinical information.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hassan, S.S.A., Bong, D.B.L. & Premsenthil, M. Detection of Neovascularization in Diabetic Retinopathy. J Digit Imaging 25, 437–444 (2012). https://doi.org/10.1007/s10278-011-9418-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-011-9418-6