Classification of pathology in diabetic eye disease

Proliferative diabetic retinopathy is a complication of diabetes that can eventually lead to blindness. Early identification of this complication reduces the risk of blindness by initiating timely treatment. We report the utility of pattern analysis tools linked with a simple linear discriminant analysis that not only identifies new vessel growth in the retinal fundus but also localises the area of pathology. Ten fluorescein images were analysed using seven feature descriptors including area, perimeter, circularity, curvature, entropy, wavelet second moment and the correlation dimension. Our results indicate that traditional features such as area or perimeter measures of neovascularisation associated with proliferative retinopathy were not sensitive enough to detect early proliferative retinopathy (SNR = 0.76, 0.75 respectively). The wavelet second moment provided the best discrimination with a SNR of 1.17. Combining second moment, curvature and global correlation dimension provided a 100% discrimination (SNR = 1)

Comparison of various methods to delineate blood vessels in retinal images

The blood vessels in the human retina are easily visualisable via digital fundus photography and provide an excellent window to the health of a patient affected by diseases of blood circulation such as diabetes. Diabetic retinopathy is identifiable through lesions of the vessels such as narrowing of the arteriole walls, beading of venules into sausage like structures and new vessel growth as an attempt to reperfuse ischaemic regions. Automated quantification of these lesions would be beneficial to diabetes research and to clinical practice, particularly for eye-screening programmes for the detection of eye-disease amongst diabetic persons

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change

Retinal Image Quality Analysis For Automatic Diabetic Retinopathy Detection

Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fund us makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fund us images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%. © 2012 IEEE.229236Saaddine, J., Honeycutt, A., Narayan, K., Zhang, X., Klein, R., Boyle, J., Projection of diabetic retinopathy and other major eye diseases among people with diabetes mellitus: United states, 2005-2050 (2008) Arch Ophthalmol., 126 (12), pp. 1740-1747Spurling, G., Askew, D., Hansar, N.H.N., Cooney, A., Jackson, C., Retinal photography for diabetic retinopathy screening in indigenous primary health care: The inala experience (2010) Australian and New Zealand Journal of Public Health, 34, pp. S30-S33Pettitt, D.J., Wollitzer, A.O., Jovanovic, L., He, G., Ipp, E., Decreasing the risk of diabetic retinopathy in a study of case management: The California medi-cal type 2 diabetes study (2005) Diabetes Care, 28 (12), pp. 2819-2822. , http://care.diabetesjournals.org/cgi/reprint/28/12/2819, DOI 10.2337/diacare.28.12.2819Bragge, P., Gruen, R., Chau, M., Forbes, A., Taylor, H., Screening for presence or absence of diabetic retinopathy: A meta-analysis (2011) Arch Ophthalmol., 129 (4), pp. 435-444Maberley, D., Morris, A., Hay, D., Chang, A., Hall, L., Mandava, N., A comparison of digital retinal image quality among photographers with different levels of training using a non-mydriatic fundus camera (2004) Ophthalmic Epidemiology, 11 (3), pp. 191-197. , DOI 10.1080/09286580490514496Philip, S., Fleming, A.D., Goatman, K.A., Fonseca, S., Mcnamee, P., Scotland, G.S., Prescott, G.J., Olson, J.A., The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme (2007) British Journal of Ophthalmology, 91 (11), pp. 1512-1517. , DOI 10.1136/bjo.2007.119453Jelinek, H., Cree, M., (2010) Automated Image Detection of Retinal Pathology, , Boca Raton: CRC PressDavis, H., Russell, S., Barriga, E., Abramoff, M., Soliz, P., Visionbased, real-time retinal image quality assessment (2009) IEEE CMBS, pp. 1-6Giancardo, L., Meriaudeau, F., Karnowski, T., Chaum, E., Tobin, K., (2010) New Developments in Biomedical Engineering, pp. 201-224. , InTech, ch. Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel DensityLalonde, M., Gagnon, L., Boucher, M.-C., Automatic visual quality assessment in optical fundus images (2001) Vision Interface, pp. 259-264Niemeijer, M., Abramoff, M.D., Van Ginneken, B., Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening (2006) Medical Image Analysis, 10 (6), pp. 888-898. , DOI 10.1016/j.media.2006.09.006, PII S1361841506000739Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J., Retinal image analysis: Concepts, applications and potential (2006) Progress in Retinal and Eye Research, 25 (1), pp. 99-127. , DOI 10.1016/j.preteyeres.2005.07.001, PII S1350946205000406Jelinek, H., Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J., Machine learning and pattern classification in identification of indigenous retinal pathology (2011) IEEE EMBSFacey, K., (2002) Health Tech. Assessment: Organisation of Services for Diabetic Retinopathy Screening, , Health Tech. Board for ScotlandFleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F., Automated assessment of diabetic retinal image quality based on clarity and field definition (2006) Investigative Ophthalmology and Visual Science, 47 (3), pp. 1120-1125. , DOI 10.1167/iovs.05-1155Winn, J., Criminisi, A., Minka, T., Object categorization by learned universal visual dictionary (2005) Proceedings of the IEEE International Conference on Computer Vision, 2, pp. 1800-1807. , DOI 10.1109/ICCV.2005.171, 1544935, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005Herbert, J., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Bossomaier, T., Rocha, A., Data fusion for multi-lesion diabetic retinopathy detection IEEE EMBS, 2012Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E., Image quality assessment: From error visibility to structural similarity (2004) IEEE Trans. on Image Processing, 13 (4), pp. 600-612Pizer Stephen, M., Amburn, E.P., Austin John, D., Cromartie, R., Geselowitz, A., Greer, T., Ter Haar Romeny, B., Zuiderveld, K., Adaptive histogram equalization and its variations (1987) Computer vision, graphics, and image processing, 39 (3), pp. 355-368Chang, C.-C., Lin, C.-J., LIBSVM: A library for support vector machines (2011) ACM Trans. on Intelligent Systems and Tech., 2, pp. 2701-2727Gonzalez, R., Woods, R., (2006) Digital Image Processing, , (3rd Ed.). Upper Saddle River, NJ, USA: Prentice-Hall, IncBay, H., Tuytelaars, T., Van Gool, L., SURF: Speeded up robust features (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3951, pp. 404-417. , DOI 10.1007/11744023-32, Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, ProceedingsSivic, J., Zisserman, A., Video google: A text retrieval approach to object matching in videos (2003) IEEE ICCV, pp. 1470-1477Do Valle Jr., E.A., (2008) Local-descriptor Matching for Image Identification Systems, , Ph.D. dissertation, Université de Cergy-Pontoise École Doctorale Sciences et Ingénierie, Cergy-Pontoise, France, JuneRocha, A., Papa, J., Meira, L., How far do we get using machine learning black-boxes? Intl. Journal of Pattern Recognition and Artificial Intelligence, 2012, pp. 1-

FABRICATION OF EBR-II, CORE-I FUEL PINS

A total of 11,117 enriched uranium-5 wt.% fissium alloy fuel pins was manufactured for EBR-II, Core I. These were made from a synthetic fission product alloy of nonradioactive elements, natural uranium, and enriched uranium. The material was supplied as precast billets. The manufacturing methods were developed for the EBR-III Fuel Cycle Facility. Experimental refabrication equipment was used to production test both the methods and the equipment. The billets were induction melted and pressure cast into precision-bore, high-silica glass molds in batches of 90 to 160. The number of molds used was adjusted according to batch weight. After casting, the molds were broken away and the castings were fed into a pin-process and inspection machine. Both ends were sheared from the castings to produce finished pins measuring 0.144 in. in diameter by 14.22 in. long. The pins were inspected for diameter, porosity, weight, and length. Rejected pins and sheared ends were broken into short lengths and returned for consolidation. Acceptable fuel pins were sealed and sodium bonded in stainless steel jackets, and assembled into Core-I fuel elements. (auth

Machine Learning And Pattern Classification In Identification Of Indigenous Retinal Pathology

Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance. © 2011 IEEE.59515954Mitchell, P., Foran, S., Wong, T.Y., Chua, B., Patel, I., Ojaimi, E., (2008) Guidelines for the Management of Diabetic Retinopathy, , Canberra: NHMRCJelinek, H.F., Cornforth, D., Cree, M., Cesar R M, J., Leandro, J.J.G., Soares, J.V.B., Mitchell, P., Automated characterisation of diabetic retinopathy using mathematical morphology: A pilot study for community health (2003) NSW Primary Health Care Research and Evaluation Conference, p. 48. , SydneyCree, M.J., Olson, J.A., McHardy, K., Sharp, P., Forrester, J., A fully automated comparative microaneurysm digital detection system (1997) Eye, 11, pp. 622-628Karperien, A.L., Jelinek, H.F., Leandro, J.J.G., Soares, J.V.B., Cesar R M, J., Luckie, A., Automated detection of proliferative retinopathy in clinical practice (2008) Clinical Ophthalmology, 2, pp. 109-122Wang, H., Hsu, W., Goh, K.G., Lee, M.L., An effective approach to detect lesions in colour retinal images (2000) IEEE Int. Conf. in Computer Vision and Pattern Recognition, pp. 181-187Streeter, L., Cree, M.J., Microaneurysm detection in colour fundus images (2003) Image and Vision Computing, pp. 280-284Goatman, K.A., Cree, M.J., Olson, J.A., Sharp, P.F., Forrester, J.V., Automated measurement of microaneurysm turnover (2003) Investigative Ophthalmology and Visual Science, 44, pp. 5335-5341Cree, M.J., Gamble, E., Cornforth, D.J., Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images (2005) Workshop on Digital Image Computing, pp. 163-169. , Brisbane, AustraliaValle, E., Cord, M., Philipp-Foliguet, S., High-dimensional descriptor indexing for large multimedia databases (2008) ACM Intl. Conf. on Information and Knowledge Management, pp. 739-748Bay, H., Tuytelaars, T., Gool, L.V., SURF: Speeded up robust features (2006) European Conf. on Computer Vision, pp. 1-14Viola, P., Jones, M., Robust real-time face detection (2004) Intl. Journa of Computer Vision, 52, pp. 137-154Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J., (2011) Points of Interest and Visual Dictionary for Retina Pathology Detection, , Technical Report IC-11-07, Institute of Computing, Univ. of Campinas, Campinas, Brazi

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semisoft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2±2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors. © 2014 Pires et al.96Sinthanayothin, C., Boyce, J.F., Williamson, T.H., Cook, H.L., Mensah, E., Lal, S., Usher, D., Automated detection of diabetic retinopathy on digital fundus images (2002) Diabetic Medicine, 19 (2), pp. 105-112. , DOI 10.1046/j.1464-5491.2002.00613.xJelinek, H.F., Cree, M.J., Worsley, D., Luckie, A.P., Nixon, P., An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice (2006) Clinical and Experimental Optometry, 89, pp. 299-305Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F., Automated microaneurysm detection using local contrast normalization and local vessel detection (2006) IEEE Transactions on Medical Imaging, 25 (9), pp. 1223-1232. , DOI 10.1109/TMI.2006.879953, 1677728Niemeijer, M., Van Ginneken, B., Russell, S.R., Suttorp-Schulten, M.S.A., Abramoff, M.D., Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis (2007) Investigative Ophthalmology and Visual Science, 48 (5), pp. 2260-2267. , DOI 10.1167/iovs.06-0996Giancardo, L., Mériaudeau, F., Karnowski, T.P., Tobin, K.W., Li, Y., Microaneurysms Detection with the Radon Cliff Operator in Retinal Fundus Images (2010) SPIE Medical Imaging, pp. 76230U-76230U. , International Society for Optics and PhotonicsAntal, B., Hajdu, A., An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading (2012) IEEE Transactions on Biomedical Engineering, 59 (6), pp. 1720-1726Lazar, I., Hajdu, A., Retinal Microaneurysm Detection Through Local Rotating Cross-section Profile Analysis (2013) IEEE Transactions on Medical Imaging, 32 (2), pp. 400-407Zhang, B., Wu, X., You, J., Li, Q., Karray, F., Hierarchical Detection of Red Lesions in Retinal Images by Multiscale Correlation Filtering (2009) SPIE Medical Imaging, pp. 72601L-72601L. , International Society for Optics and PhotonicsSánchez, C.I., Hornero, R., Mayo, A., García, M., Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images (2009) SPIE Medical Imaging, pp. 72601M-72601M. , International Society for Optics and PhotonicsSánchez, C.I., García, M., Mayo, A., López, M.I., Hornero, R., Retinal image analysis based on mixture models to detect hard exudates (2009) Medical Image Analysis, 13 (4), pp. 650-658Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Garg, S., Tobin, K.W., Chaum, E., Exudate-based diabetic macular edema detection in fundus images using publicly available datasets (2012) Medical Image Analysis, 16 (1), pp. 216-226Fleming, A.D., Philip, S., Goatman, K.A., Williams, G.J., Olson, J.A., Sharp, P.F., Automated detection of exudates for diabetic retinopathy screening (2007) Physics in Medicine and Biology, 52 (24), pp. 7385-7396. , DOI 10.1088/0031-9155/52/24/012, PII S0031915507570430Sopharak, A., Uyyanonvara, B., Barman, S., Williamson, T.H., Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods (2008) Computerized Medical Imaging and Graphics, 32, p. 8Welfer, D., Scharcanski, J., Marinho, D.R., A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images (2010) Computerized Medical Imaging and Graphics, 34, pp. 228-235Boureau, Y., Bach, F., LeCun, Y., Ponce, J., Learning mid-level features for recognition (2010) IEEE Intl. Conference on Computer Vision and Pattern RecognitionRocha, A., Carvalho, T., Jelinek, H.F., Goldenstein, S., Wainer, J., Points of interest and visual dictionaries for automatic retinal lesion detection (2012) IEEE Transactions on Biomedical Engineering, 59, pp. 2244-2253Jelinek, H.F., Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J., Machine learning and pattern classification in identification of indigenous retinal pathology (2011) Intl. Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5951-5954Jelinek, H.F., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Data fusion for multi-lesion diabetic retinopathy detection (2012) IEEE Intl. Computer-Based Medical Systems, pp. 1-4Phillips, P.J., Visible manifestations of diabetic retinopathy (2012) Medicine Today, 5, p. 83(2013) Diabetes Programme. Online, , http://www.who.int/diabetes/en, World Health Organization Available: Accessed 6 May 2014Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Tobin, K., Microaneurysm detection with radon transform-based classification on retina images (2011) Intl. Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5939-5942Li, Y., Karnowski, T.P., Tobin, K.W., Giancardo, L., Morris, S., A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy (2011) Telemedicine and E-Health, 17, pp. 627-634Cree, M.J., Gamble, E., Cornforth, D.J., Colour normalisation to reduce interpatient and intrapatient variability in microaneurysm detection in colour retinal images (2005) Workshop on Digital Image Computing, pp. 163-168Soares, J.V.B., Leandro, J.J.G., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J., Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification (2006) IEEE Transactions on Medical Imaging, 25 (9), pp. 1214-1222. , DOI 10.1109/TMI.2006.879967, 1677727Acharya, U.R., Lim, C.M., Ng, E.Y.K., Chee, C., Tamura, T., Computer-based detection of diabetes retinopathy stages using digital fundus images (2009) Journal of Engineering in Medicine, 223, pp. 545-553Gonzalez, R.C., Woods, R.E., (2006) Digital Image Processing, , Upper Saddle River, NJ, USA: PrenticeHall, Inc., 2nd editionNayak, J., Bhat, P.S., Acharya, U.R., Lim, C.M., Kagathi, M., Automated identification of diabetic retinopathy stages using digital fundus images (2008) Journal of Medical Systems, 32, pp. 107-115Jelinek, H.F., Al-Saedi, K., Backlund, L.B., Computer assisted 'top-down' assessment of diabetic retinopathy (2009) World Congress on Medical Physics and Biomedical Engineering, pp. 127-130Yun, W.L., Rajendra, A.U., Venkatesh, Y.V., Chee, C., Min, L.C., Ng, E.Y.K., Identification of different stages of diabetic retinopathy using retinal optical images (2008) Information Sciences, 178 (1), pp. 106-121. , DOI 10.1016/j.ins.2007.07.020, PII S0020025507003635Sivic, J., Zisserman, A., Video Google: A Text Retrieval Approach to Object Matching in Videos (2003) IEEE Intl. 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Symposium on Biomedical Imaging: From Nano to Macro, pp. 1349-1352Deepak, K.S., Sivaswamy, J., Automatic Assessment of Macular Edema from Color Retinal Images (2012) IEEE Transactions on Medical Imaging, 31 (3), pp. 766-776Pires, R., Jelinek, H.F., Wainer, J., Goldenstein, S., Valle, E., Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection (2013) IEEE Transactions on Biomedical Engineering, 60 (12), pp. 3391-3398Dietterich, T.G., Approximate statistical tests for comparing supervised classification learning algorithms (1998) Neural Computation, 10, pp. 1895-1923Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance (1937) Journal of the American Statistical Association, 32 (200), pp. 675-701Nemenyi, P., (1963) Distribution-free Multiple Comparisons, , Doctoral dissertation, Princeton UniversityKlein, R., Klein, B.E.K., Moss, S.E., Davis, M.D., DeMets, D.L., The Wisconsin Epidemiologic Study of Diabetic Retinopathy. 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ETDRS Report Number 10 (1991) Ophthalmology, 98, pp. 786-806. , ETDRS Research GroupMitchell, P., Foran, S., Wong, T.Y., Chua, B., Patel, I., (2008) Guidelines for the Management of Diabetic Retinopathy, , http://www.nhmrc.gov.au/_files_nhmrc/file/publications/synopses/di15.pdf, Available: Accessed: 6 May 2014Chew, E.Y., A simplified diabetic retinopathy scale (2003) Ophthalmology, 110 (9), pp. 1675-1676. , SepAbramoff, M.D., Niemeijer, M., Suttorp-Schulten, M.S.A., Viergever, M.A., Russell, S.R., Van Ginneken, B., Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes (2008) Diabetes Care, 31 (2), pp. 193-198. , http://care.diabetesjournals.org/cgi/reprint/31/2/193, DOI 10.2337/dc07-1312Fleming, A.D., Goatman, K.A., Philip, S., Williams, G.J., Prescott, G.J., The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy (2010) British Journal of Ophthalmology, 94 (6), pp. 706-711Abramoff, M.D., Folk, J.C., Han, D.P., Walker, J.D., Williams, D.F., Automated analysis of retinal images for detection of referable diabetic retinopathy (2013) JAMA Ophthalmol, 131 (3), pp. 351-357Ahmed, J., Ward, T.P., Bursell, S.-E., Aiello, L.M., Cavallerano, J.D., Vigersky, R.A., The sensitivity and specificity of nonmydriatic digital stereoscopic retinal imaging in detecting diabetic retinopathy (2006) Diabetes Care, 29 (10), pp. 2205-2209. , http://care.diabetesjournals.org/cgi/reprint/29/10/2205.pdf, DOI 10.2337/dc06-0295Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A., The devil is in the details: An evaluation of recent feature encoding methods (2011) British Machine Vision Conference (BMVC), pp. 1-12Perronnin, F., Akata, Z., Harchaoui, Z., Schmid, C., Towards good practice in large-scale learning for image classification (2012) Intl. 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