Abstract
Automated retinal image processing is becoming a primary important screening tool for early detection of diabetic retinopathy (DR). An automated system reduces human errors and also reduces the burden on the ophthalmologists. The accurate detection of microaneurysms (MAs) is an important step for early detection of DR. This paper present some methods to improve the quality of input retinal image and extraction of blood vessels, as a preprocessing step in automatic early stage detection of DR. Experimental results are performed for preprocessing and blood vessel extraction techniques using standard fundus image database.
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References
Chiulla TA, Amador AG, Zinman B (2003) Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. Diabetes Care 26(9):2653–2664
Frank RN (1995) Diabetic retinopathy. Prog Retin Eye Res 14(2):361–392
Klein R, Klein BEK, Moss SE (1994) Visual impairment in diabetes. Ophthalmology 91:1–9
Klonoff DC, Schwartz DM (2000) An economic analysis of interventions for diabetes. Diabetes Care 23(3):390–404
Center for Disease Control and Prevention (2011) National diabetes fact sheet: technical report, U.S.
Bresnick GH, Mukamel DB, Dickinson JC, Cole DR (2000) A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Opthalmology 107(1):19–24
Susman EJ, Tsiaras WJ, Soper KA (1982) Diagnosis of diabetic eye disease. J Am Med Assoc 247(23):3231–3234
Hatanaka Y, Inoue T, Okumura S, Muramatsu C, Fujita S (2012) Automated microaneurysm detection method based on double-ring filter and feature analysis in retinal fundus images. In: Proceedings of 25th IEEE international symposium on computer-based medical systems, paper-150
Saleh MD, Eswaran C (2012) An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. Elsevier—Comput Meth Programs Biomed 108:186–196
Marín D, Aquino A, Gegúndez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30(1):146–158
El Abbadi NK, Al Saadi EH (2013) Blood vessels extraction using mathematical morphology. J Comput Sci 9(10):1389–1395
Ram K, Joshi GD, Sivaswamy J (2011) A successive clutter-rejection-based approach for early detection of diabetic retinopathy. IEEE Trans Biomed Eng 58(3)
Masroor AM, Mohammad DB (2008) Segmentation of brain MR images for tumor extraction by combining K means clustering and Perona-Malik anisotropic diffusion model. Int J Image Proc 2(1)
Dey N, Roy AB, Pal M, Das A (2012) FCM based blood vessel segmentation method for retinal images. Int J Comput Sci Netw (IJCSN) 1(3). ISSN:2277-5420
Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509
Image Sciences Institute (2001) DRIVE: digital retinal images for vessel extraction. http://www.isi.uu.nl/Research/Databases/DRIVE
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Mane, V.M., Jadhav, D.V., Kawadiwale, R.B. (2015). Preprocessing in Early Stage Detection of Diabetic Retinopathy Using Fundus Images. In: Gupta, S., Bag, S., Ganguly, K., Sarkar, I., Biswas, P. (eds) Advancements of Medical Electronics. Lecture Notes in Bioengineering. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2256-9_3
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DOI: https://doi.org/10.1007/978-81-322-2256-9_3
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