Drusen Quantification for Early Identification of Age Related Macular Degeneration
DOI:
https://doi.org/10.14738/aivp.33.1291Keywords:
Age-related macular degeneration, pixel-wise feature extraction, drusen subtypes, quantification, fundus images.Abstract
Age-related macular degeneration (AMD) is a degenerative disorder in people of age 50 and above, in developed nations, characterized on grading of color fundus images by the presence of pathologies such as drusen in macular area. Currently, there is no treatment which can cure irreversible blindness due to age-related macular degeneration. Therefore, the only feasible option is to prevent the incidence of age-related macular degeneration and avoid this unnecessary vision loss. This paper presents an automated method for early diagnosis of AMD by quantifying drusen on the basis of its size, number and area in macular region from standard color retinal images. Previously used methods, generating unsatisfactory results in some cases, are time consuming, complex and prone to error. Therefore, this paper provides a simple drusen detection and quantification method to detect the exact number of drusen , area and size as well as classify drusen into small, intermediate and soft or large which will further help in initial screening of early stage of age-related macular degeneration and its progression i.e. change in drusen area. The proposed method achieved 93.2% accuracy for drusen detection and 91.8% accuracy in small drusen, 98.66% in intermediate drusen and 92.91% in soft drusen quantification in order to grade the severity of AMD which outwits the other methods.
References
(1) Lim LS, Mitchell P, Seddom JM et al. Age-related macular degeneration.t, 2012. 379: 1728-1738.
(2) Bartlett and Eperjesi. use of fundus imaging in quantification of age-related macular degeneration. Surv Ophthalmol, 2007. 52: 655-671.
(3) Wong, Liew et al. clinical update: new treatments for age-related macular degeneration. Lancet, 2007. 370: 204-206.
(4) Wong and Rogers. Statins and age-related macular degeneration: time for a randomized controlled trial? Am J Ophthalmol, 2007. 144: 117-119.
(5) Age-related Eye Disease Study Research Group. The age-related eye disease study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the age-related eye disease study report number 6. 2001;132:668-681.
(6) De Jong, Age-related macular degeneration. The New England Journal of Medicine, 355(14), 2006. p: 1474-1485.
(7) Kanagasingam, Bhuiyan et al. Progress on retinal image analysis
for age related macular degeneration, Progress in Retinal Eye Research, 2014. 38:20–42.
(8) Lim, Laude et al. Age-related macular degeneration: an Asian perspective. Ann. Acad. Med. Singapore, 2007. 36 (10), S15.
(9) Mitchell, Smith, Attebo et al. Prevalence of age-related maculopathy in Australia. The Blue Mountains eye study, Ophthalmology, 1995. 102 (10):1450–1460.
(10) A.-R.E.D.S.R. Group.A randomized, placebo-controlled, clinical trial of highdose supplementation with vitamins c and e, beta carotene, and zinc for agerelated macular degeneration and vision loss: AREDS report
no. 8, Arch. Ophthalmology, 2001. 119 (10):1417–1436.
(11) Wong, Chakravarthy et al. The natural history and prognosis of neovascular age-related macular degeneration: a systematic review of the literature and meta-analysis. Ophthalmology, 2008. 115: 116-126.
(12) Chopdar, Chakravarthy et al. Age related macular degeneration.British Medical Journal, 2003. 326 (7387):485–488.
(13) Mookiah, Acharyaet al. Computer-aided diagnosis of diabetic retinopathy: A review. Computer in Biology and Medicine, 2013. 43 (12):2136–2155.
(14) Bird, Bressler et al. An international classification and grading system for age-related Maculopathy and age-related macular degeneration. The International ARM Epidemiological Study Group. Surv Ophthalmol, 1995. 39: 367-374.
(15) Brandon and Hoover. Drusen Detection in a Retinal Image Using Multi- Level Analysis. LNCS, 2003. 618-625.
(16) Barriga, Murray, Agurto, Pattichis et al. Multiscale AM-FM for lesion phenotyping on age-related macular degeneration. INSPEC, 2009. p: 1-5.
(17) Agurto, Barriga, Murray et al. Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Invest Ophthalmol Vis Sci, 2011. 52: 5862-5871.
(18) Prasath and Ramaya. Detection of macular drusen based on texture descriptors. Research journal of information technology, 2015. 7 (1): 70-79.
(19) Soliz, Wilson, Nemeth and Nguyen P. Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy. SPIE, 2002. 4681:159-170.
(20) Smith, Nagasaki, Sparrow et al. A method of drusen measurement based on the geometry of fundus reflectance. Biomedical Engineering, 2003. Online 2: 10.
(21) Kumari and Mittal. Automated Drusen Detection Technique for Age-Related Macular Degeneration. Journal of biomedical engineering in medical imagiong, 2015. 2:18-26.
(22) Hanafi, Hijazi, Coenen and Zheng. Retinal Image Classification for the Screening of Age-related Macular Degeneration. In: SGAI International Conference on Artificial Intelligence, 2010. p: 325-338.
(23) Mora, Vieira, Manivannan and Fonseca. Automated drusen detection in retinal images using analytical modelling algorithms. Biomedical Engineering Online, 2011. p: 10: 59.
(24) Bhuiyan, Kawasaki, Sasaki et al. Drusen detection and quantification for early identification of Age-related macular degeneration using color fundus imaging. Clinical and experimental ophthalmology, 2013. 4: 305. DOI: 10.4172/2155-9570.1000305.
(25) STARE database. Available at http://www.ces.clemson.edu/~ahoover/stare.
(26) ARIA database. Available at http://www.eyecharity.com/aria_online.
(27) Klein, Devis et al. The Wisconsin age-related Maculopathy grading system. Ophthalmology, 1991. 98: 1128-1134.
(28) Bartlett and Eperjesi. Use of fundus imaging in quantification of age-related macular change. Surv Ophthalmol, 2007. 52: 655-671.
(29) Age-Related Eye Disease Study Research Group (1999) The Age-Related Eye Disease Study (AREDS): design implications. AREDS report no. 1. Control Clin Trials 20: 573-600.
(30) Mittal, Kumar et al. Neural Network based focal liver lesion diagnosis using ultrasound images. Computerized Medical Imaging and Graphics, 2011. 35:315-323.
(31) Chugh, Kaur and Mittal. Exudates segmentation in retinal fundus images for the detection of diabetic retionapthy. International journal of engineering research and technology, 2014. 3: 673-677.
(32) Mittal, Kumar, Saxenaet al. Enhancement of ultrasound images by modified diffusion method, Medical and Biological Engineering and Computing, 2010. 48(12): 1281-1291.
(33) Kaur and Mittal. Segmentation and measurement of exudates in fundus images of the retina for detection of retinal diseases. Journal of biomedical engineering and medical imaging, 2015. 2: 27-38.
