diabetestalk.net

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol

Ijca - Automatic Severity Level Classification Of Diabetic Retinopathy

Ijca - Automatic Severity Level Classification Of Diabetic Retinopathy

Home Archives Volume 180 Number 12 Automatic Severity Level Classification of Diabetic Retinopathy IJCA solicits original research papers for the May 2018 Edition. Last date of manuscript submission is April 20, 2018. Read More Automatic Severity Level Classification of Diabetic Retinopathy International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA Jissmol James, Ershad Sharifahmadian and Liwen Shih. Automatic Severity Level Classification of Diabetic Retinopathy. International Journal of Computer Applications 180(12):30-35, January 2018. BibTeX @article{10.5120/ijca2018916244, author = {Jissmol James and Ershad Sharifahmadian and Liwen Shih}, title = {Automatic Severity Level Classification of Diabetic Retinopathy}, journal = {International Journal of Computer Applications}, issue_date = {January 2018}, volume = {180}, number = {12}, month = {Jan}, year = {2018}, issn = {0975-8887}, pages = {30-35}, numpages = {6}, url = {doi = {10.5120/ijca2018916244}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA}} Diabetic Retinopathy (DR) is a major cause of blindness, when a disease strikes the retina due to diabetes. Early detection of retinopathy can rescue patients from vision loss. Therefore, in this paper we propose an automatic severity level assessment of the diabetic retinopathy using innovative image processing techniques combined with a multi-layered artificial neural network model for classification of retina images. The color retina images are collected from the standard DIARECTDB1 and MESSIDOR datasets. The collected data includes the images of normal eyes, as well as the images of mild, moderate and severe cases of Non-Proliferative Diabetic Retinopathy (NPDR). First, the lesions on the retina Continue reading >>

Diaretdb1 Techreport V 1 1

Diaretdb1 Techreport V 1 1

DIARETDB1 diabetic retinopathy database and evaluation protocol Tomi Kauppi1 , Valentina Kalesnykiene2, Joni-Kristian Kamarainen1 , Lasse Lensu1 , Iiris Sorri2 , Asta Raninen2 , Raija Voutilainen2, Hannu Uusitalo3, a4 Heikki K alvi ainen1 and Juhani Pietil Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology, Finland Department of Ophthamology Faculty of Medicine, University of Kuopio, Finland For a particularly long time, automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justied by the excellent potential for new products in the medical industry and signicant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a veried ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice. Diabetes has become one of the rapidly increasing health threats worldwide [21]. Only in Finland, there are 30 000 people diagnosed to the type 1 maturity onset diabetes in the young, and 200 000 people diagnosed to the type 2 latent autoimmune diabetes in adults [4]. In addition, the current estimate predicts that there are 50 000 undiagnosed patients [4]. Proper and early treatment of diabetes is cost effective since the implications of poor or Continue reading >>

The Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol - Converis Research Information System By Thomson Reuters: - Converis Standard Config

The Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol - Converis Research Information System By Thomson Reuters: - Converis Standard Config

The DIARETDB1 diabetic retinopathy database and evaluation protocol Authors: Kauppi Tomi, Kalesnykiene Valentina, Kmrinen Joni-Kristian, Lensu Lasse, Sorri Iiris, Raninen Asta, Voutilainen Raija, Pietil Juhani, Klviinen Heikki, Uusitalo Hannu Automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justified by the excellent potential for new products in the medical industry and significant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a verified ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice. Continue reading >>

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol

... We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha [48], and for screening and need for referral on MESSIDOR [49] compared to a second human expert. An extensive analysis of the complementarity of the deep learned features with respect to the hand crafted ones is also provided, with the purpose of assessing their contribution in the discrimination process. ... ... Automated methods for computer-aided diagnosis are known to significantly re- duce the time, cost, and effort of DR screening: their high throughput ensures the more efficient analysis of large populations [176]. They also reduce the intra- Figure 5.1: Examples of red lesions observed in fundus photographs from DI- ARETDB1 . expert variability, which is commonly high due to the small size and the irregular shape of the lesions [3]. ... ... Further details about the experimental setup are provided in Table 5.4. DIARETDB1 consists of 89 color fundus images taken under varying imaging settings . 84 images contain signs of mild or pre-proliferative DR, and the re- maining 5 are considered normal. ... Continue reading >>

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol

DIARETDB1 diabetic retinopathy database and evaluation protocol (Lappeenranta University of Technology)Valentina Kalesnykiene6 (University of Eastern Finland)Joni-Kristian Kamarainen17 (Tampere University of Technology)Lasse Lensu10 (Lappeenranta University of Technology)Iiris Sorri6 (University of Eastern Finland)A. Raninen2 (Lappeenranta University of Technology)Hannu Uusitalo28 Automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justified by the excellent potential for new products in the medical industry and significant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a verified ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice. Continue reading >>

Scitepress - Science And Technology Publications

Scitepress - Science And Technology Publications

A TWO-PHASE PRE-FILTERING APPROACH TO THE AUTOMATIC SCREENING OF DIGITAL FUNDUS IMAGES Blint Antal, Andrs Hajdu, Adrienne Csutak, Tnde Pet In this paper, we present an approach to decrease the computational burden of an automatic screening system designed for diabetic retinopathy. The proposed method consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on their abnormality. If an image is found to be abnormal, it will not be analyzed further with robust lesion detector algorithms. As an improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions which contain possible lesions for images that have been passed pre-screening. These regions will serve as inputs to lesion detectors later on, which can achieve better computational performance by operating on specific regions only instead of the entire image. Experimental results show that both two steps of the proposed approach are valid to efficiently exclude a large amount of data from further processing to improve the performance of an automatic screening system. Abramoff, M., Niemeijer, M., Suttorp-Schulten, M., Viergever, M. A., Russel, S. R., and van Ginneken, B. (February 2008). Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care, 31:193-198. Fleming, A. D., Philip, S., and Goatman, K. A. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Transactions on Medical Imaging, 25(9):1223-1232. Kauppi, T., Kalesnykiene, V., Kmrinen, J., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kl Continue reading >>

Openaire - Publication: Constructing Benchmark Databas...

Openaire - Publication: Constructing Benchmark Databas...

Zou, KH. Receiver operating characteristic (roc) literature research. Fawcett, T. An introduction to roc analysis. Pattern Recognition Letters . 2006; 27 (8): 861-874 Diabetic retinopathy database and evaluation protocol (DIARETDB1). Figueiredo, MAT, Jain, AK. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2002; 24 (3): 381-396 Paalanen, P, Kamarainen, J-K, Ilonen, J, Klviinen, H. Feature representation and discrimination based on Gaussian mixture model probability densitiespractices and algorithms. Pattern Recognition . 2006; 39 (7): 1346-1358 Structured analysis of the retina (STARE). Hoover, A, Goldbaum, M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging . 2003; 22 (8): 951-958 Digital retinal images for vessel extraction (DRIVE). Niemeijer, M, Staal, J, van Ginneken, B, Loog, M, Abramoff, MD. Comparative study of retinal vessel segmentation a new publicly available database. Medical Imaging: Image Processing . 2004: 648-656 Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR). Collection of multispectral images of the fundus (CMIF). Styles, IB, Calcagni, A, Claridge, E, Orihuela-Espina, F, Gibson, JM. Quantitative analysis of multi-spectral fundus images. Medical Image Analysis . 2006; 10 (4): 578-597 Scharstein, D, Szeliski, R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision . 2002; 47 (13): 7-42 Review: retinal vessel image set for estimation of widths (REVIEW). Al-Diri, B, Hunter, A, Steel, D, Habib, M, Hudaib, T, Berry, S. Reviewa reference data set for retinal vessel profiles. : 2262-2265 Ru Continue reading >>

Detection Of Hard Exudates In Diabetic Retinopathy Retinal Images By Utilizing Visual Dictionary And Classifier Approaches | Akyol | Mugla Journal Of Science And Technology

Detection Of Hard Exudates In Diabetic Retinopathy Retinal Images By Utilizing Visual Dictionary And Classifier Approaches | Akyol | Mugla Journal Of Science And Technology

DETECTION OF HARD EXUDATES IN DIABETIC RETINOPATHY RETINAL IMAGES BY UTILIZING VISUAL DICTIONARY AND CLASSIFIER APPROACHES Diabetic retinopathy is a disease that causes blindness resulting from damages that emerge in the retina depending on the diabetes mellitus. There are two stages of the disease including the non-proliferative and proliferative. Eyesight loss is blocked by means of early detection and diagnosis of non-proliferative DR findings. In this study, we designed a decision support system for automatic detection of hard exudates which are early stage DR lesions. This system consists of region-of-interest, feature extraction, visual dictionary and classifying stages. We tested the performance of the system, which we carried out based on system learning and analysis of new retinal images, on the public DIARETDB1 retinal image dataset. Experimental results showed us that machine learning technique suggested by us is successful. Mohamed, Q., Gillies, M.C., Wong, T.Y., Management of diabetic retinopathy: A systematic review, Jama-J Am Med Assoc, 298(8), 902-916, 2007. Venkatesan, R., Chandakkar, P., Li, B., Li, H.K., Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features, Conf Proc IEEE Eng Med Biol Soc, 1462-1465, San Diego, 2012. Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Pietila, J., Kalviainen, H., Uusitalo, H., Diaretdb1 diabetic retinopathy database and evaluation protocol, Proceedings of the Medical Image Understanding and Analysis, 2007. Chen, X., Bu, W., Wu, X., Dai, B., Teng, Y., A novel method for automatic hard exudates detection in color retinal images, in IEEE International Conference on Machine Learning and Cybernetics, 1 Continue reading >>

Diabetic Retinopathy Database And Evaluation Protocol (diaretdb1), Electronic Material (online).

Diabetic Retinopathy Database And Evaluation Protocol (diaretdb1), Electronic Material (online).

Diabetic retinopathy database and evaluation protocol (DIARETDB1), Electronic material (Online). 1Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey 2Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Yldrm Beyazt University, Turkey 3Department of Ophthalmology, Faculty of Medicine, Hacettepe University, Turkey Copyright 2016 Science and Education Publishing Kemal AKYOL, afak BAYIR, Baha EN, Hasan B. AKMAK. Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors. Journal of Computer Sciences and Applications. 2016; 4(3):59-66. doi: 10.12691/jcsa-4-3-2. Correspondence to: Kemal AKYOL, Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey. Email: [email protected] Diabetic retinopathy is one of the main complications of diabetes mellitus and it is a progressive ocular disease, the most significant factor contributing to blindness in the later stages of the disease. It has been a subject of many studies in the medical image processing field for a long time. Hard exudates are one of the primary signs of early stage diabetic retinopathy diagnosis. Immediately identifying hard exudates is of great importance for the blindness and coexistent retinal edema. There are various ways of achieving meaningful information from an image and one of them is key point extraction method. In this study, we presented a technique based on the acquisition of important information by utilizing the description information about the image within the framework of the learning approach in order to identify hard exudates. This technique includes the learning and testing processes of the system in order to make the right decisions in th Continue reading >>

Segmentation Of Exudates And Optic Disc In Retinal Images

Segmentation Of Exudates And Optic Disc In Retinal Images

Segmentation of Exudates and Optic Disc in Retinal Images 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing,535-542,2008 Giri Babu Kande - S.R.K. Institute of Technology Vijayawada Andhra Pradesh India P. Venkata Subbaiah - Amrita Sai Institute of Science & Technology Paritala Andhra Pradesh India T. Satya Savithri - Jawaharlal Nehru Technological University, Hyderabad This paper proposes two efficient approaches for automatic detection and extraction of Exudates and Optic disk in ocular fundus images. The localization of optic disk is composed of three steps. First the centre of optic disk is estimated by finding a point that has maximum local variance. The color morphology in Lab space is used to have homogeneous optic disk region. The boundary of the optic disk is located using geometric active contour with variational formulation. The Exudates identification involves Preprocessing, Optic disk elimination, and Segmentation of Exudates. In Exudates detection the enhanced segments are extracted based on Spatially Weighted Fuzzy c-Means clustering algorithm. The Spatially Weighted Fuzzy c-Means clustering algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. The Experimental results of both approaches are validated with ground truth images. The proposed algorithm for optic disk detection produces 92.53% accuracy. The sensitivity and the specificity of the proposed algorithm for exudates detection are 86% and 98% respectively. 2018 Digital Science & Research Solutions, Inc. All Rights Reserved | About us Privacy policy Legal terms VPAT Citation Count is the number of times that this paper has been cited by other published papers in the database. The Altmetric Attention Score i Continue reading >>

Diaretdb1 - Standard Diabetic Retinopathy Database

Diaretdb1 - Standard Diabetic Retinopathy Database

Copyright IMAGERET project. All rights reserved. Last modified: $Date: 2007/06/19 10:03:24 $ DIARETDB1 - Standard Diabetic Retinopathy Database This is a public database for benchmarking diabetic retinopathydetection from digital images. The main objective of the designhas been to unambiguously define a database and atesting protocol which can be used to benchmark diabetic retinopathydetection methods. By using this database and the defined testingprotocol, the results between different methods can be compared. Formore information refer to the documentation. The database can be freely downloaded and used for scientific research purposes. You are not allowed redistribute or modify it. Proper citing of this resource is expected if the database is used in research or other reporting. The database is meant to be useful, but it is distributed WITHOUT ANY WARRANTY, and without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Inclusion of this database or even parts of it in a proprietary program is not allowed without a written permission from the owners of the copyright. If you wish to obtain such a permission, you should contact Machine Vision and Pattern Recognition Research Group Laboratory of Information Processing Lappeenranta University of Technology PO Box 20 FI-53851 Lappeenranta FINLAND or owner-diaretdblists.lut.fi (in the address, replace the string with @, please). All communication related to the database updates and announcements of future releases will be carried out through the diaretdb mailing list. You can subscribe the list by sending a message to the address diaretdb-onlists.lut.fi by using your default e-mail system. You will get a confirmation with further instructions after your subscription. If you find any errors or Continue reading >>

Diaretdb1_techreport_v_1_1 - Diaretdb1 Diabetic Retinopathy...

Diaretdb1_techreport_v_1_1 - Diaretdb1 Diabetic Retinopathy...

DIARETDB1 diabetic retinopathy database andevaluation protocolTomi Kauppi1, Valentina Kalesnykiene2,Joni-Kristian Kamarainen1, Lasse Lensu1, Iiris Sorri2,Asta Raninen2, Raija Voutilainen2, Hannu Uusitalo3,Heikki Kalviainen1and Juhani Pietila41Machine Vision and Pattern Recognition Research GroupLappeenranta University of Technology, Finland2Department of OphthamologyFaculty of Medicine, University of Kuopio, Finland3University of Tampere4Perimetria Ltd., Helsinki, FinlandAbstractFor a particularly long time, automatic diagnosis of diabetic retinopathy from dig-ital fundus images has been an active research topic in the medical image process-ing community. The research interest is justified by the excellent potential for newproducts in the medical industry and significant reductions in health care costs.However, the maturity of proposed algorithms cannot be judged due to the lackof commonly accepted and representative image database with a verified groundtruth and strict evaluation protocol. In this study, an evaluation methodology is pro-posed and an image database with ground truth is described. The database is pub-licly available for benchmarking diagnosis algorithms. With the proposed databaseand protocol, it is possible to compare different algorithms, and correspondingly,analyse their maturity for technology transfer from the research laboratories to themedical practice.1IntroductionDiabetes has become one of the rapidly increasing health threats worldwide [21]. Onlyin Finland, there are 30 000 people diagnosed to the type 1 maturity onset diabetes inthe young, and 200 000 people diagnosed to the type 2 latent autoimmune diabetes inadults [4]. In addition, the current estimate predicts that there are 50 000 undiagnosedpatients [4]. Proper and early treatment of di Continue reading >>

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol Classroom Modifications Visual Impairment

Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol Classroom Modifications Visual Impairment

red-green color-blindness and hemophilia A linkage.Kaj je doktorat? [monitor etike] Etika znanosti.In this study, readings on a Schiotz tonometer were compared with readings with an found that tonometry (cutoff level 22 mm Hg) had a 50 percent sensitivity for detecting OAG in glaucomaAll info about the marijuana strain Lazy Eye from the breeder Illuminati Seeds!This photo shows a small hypopyon (layer of pus) in the anterior chamber of an eye that has undergone a glaucoma tube-shunt surgery.Search results for lutein at Sigma-Aldrich Compare products: Select the checkbox on up to 4 items, then click compare for a detailed product comparisonFoods Containing Lutein there are no current pumpkin, Brussels sprouts, broccoli, romaine and iceberg lettuce, asparagus and carrots are good food sources ofNearly all glaucoma medications are prescribed Latanoprost was the first prostaglandin to be approved as first-line treatment for elevated eye pressureThe black eyes I saw were dull black like charcoal, Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol Classroom Modifications Visual Impairment If not treated in its early stages. Diaretdb1 Diabetic Retinopathy Database And Evaluation Protocol Classroom Modifications Visual Impairment avoid tetracycline stains in for their babies. The American Society of Cataract and Refractive Surgery is proud to induct into the. A glowing healthy complexion thats softer to the touch! Due to the increase in phytonutrients antioxidants and carotenoids that support healthy. Patients with Graves disease are quite likely to have dry eye symptoms. Costco Free Diabetes Magazine: Save On Diabetes Products and Learn More About Lutein and zeaxanthin may help reduce your risk of macular But remember to keep an eye on portion sizes and try to la Continue reading >>

Fire: Fundus Image Registration Dataset | Hernandez-matas | Journal For Modeling In Ophthalmology

Fire: Fundus Image Registration Dataset | Hernandez-matas | Journal For Modeling In Ophthalmology

Carlos Hernandez-Matas, Xenophon Zabulis, Areti Triantafyllou, Panagiota Anyfanti, Stella Douma, Antonis A Argyros Purpose:Retinal image registration is a useful tool for medical professionals. However, performance evaluation of registration methods has not been consistently assessed in the literature. To address that, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed. Methods:The dataset is comprised by 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap between images and the presence/absence of anatomical differences. Ground truth in the form of corresponding image points and a protocol to evaluate registration performance are provided. Results:The proposed protocol is shown to enable quantitative and comparative evaluation of retinal registration methods under a variety of conditions. Conclusion:This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use. Abramoff MD, Garvin MK, Sonka M. Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 2010;3, 169208. issn: 1937-3333. doi: 10.1109/RBME.2010.2084567. Grosso A, Veglio F, Porta M, Grignolo FM, Wong TY. Hypertensive retinopathy revisited: some answers, more questions. British Journal of Ophthalmology, 2005;89(12): 16461654. doi:10.1136/bjo.2005.072546. Meitav N, Ribak EN. Improving retinal image resolution with iterative weighted shift-and-add. Journal of the Optical Society of America A, 2011;28(7): 13951402. doi: 10.1364/JO Continue reading >>

Constructing Benchmark Databases And Protocols For Medical Image Analysis: Diabetic Retinopathy

Constructing Benchmark Databases And Protocols For Medical Image Analysis: Diabetic Retinopathy

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy Tomi Kauppi ,1 Joni-Kristian Kmrinen ,2 Lasse Lensu ,1,* Valentina Kalesnykiene ,3 Iiris Sorri ,3 Hannu Uusitalo ,4 and Heikki Klviinen 1 1Machine Vision and Pattern Recognition Laboratory, Department of Mathematics and Physics, Lappeenranta University of Technology (LUT), Skinnarilankatu 34, FI-53850 Lappeenranta, Finland Find articles by Joni-Kristian Kmrinen 1Machine Vision and Pattern Recognition Laboratory, Department of Mathematics and Physics, Lappeenranta University of Technology (LUT), Skinnarilankatu 34, FI-53850 Lappeenranta, Finland 1Machine Vision and Pattern Recognition Laboratory, Department of Mathematics and Physics, Lappeenranta University of Technology (LUT), Skinnarilankatu 34, FI-53850 Lappeenranta, Finland 1Machine Vision and Pattern Recognition Laboratory, Department of Mathematics and Physics, Lappeenranta University of Technology (LUT), Skinnarilankatu 34, FI-53850 Lappeenranta, Finland 2Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 10, FI-33720 Tampere, Finland 3Department of Ophthalmology, University of Eastern Finland, Yliopistonranta 1, FI-70211 Kuopio, Finland 4Department of Ophthalmology, University of Tampere, Biokatu 14, FI-33520 Tampere, Finland Received 2013 Jan 25; Accepted 2013 May 26. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article has been cited by other articles in PMC. We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medi Continue reading >>

More in diabetes