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  1. Diagnosing Diabetic Eye Disease
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  4. Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

It is based on combining image quality control with RL detection. These systems have been successfully applied in DR screening scenarios to identify the presence of DR or referable DR.

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However, to the best of our knowledge, they are not able to identify the high-risk DR or the presence of DME yet. Early DR detection is important to slow down disease progression and avoid severe vision loss in diabetic patients. Regular DR screening is paramount to ensure timely diagnosis and treatment. However, the interpretation and grading of fundus images for this task is actually a manual process. This is a time-consuming approach, which is also subject to inter-observer variability. For this reason, automatic methods to detect DR-related lesions, as well as the development of CAD systems for DR can be a reliable option to cut down DR screening costs, to reduce the workload of ophthalmologists and to improve attention to diabetic patients.

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  • The algorithms for DR lesions detection included in this review were very heterogeneous. The validation methods and test databases of the studies were not uniform or standardized. Therefore, it was not possible to make a direct comparison of their performance. However, some results should be underlined. In the case of EXs, nine studies complied with the inclusion criteria. Most of these studies achieved the BDA figures. For example, the studies by Fleming et al. Besides, these two studies were remarkable because they included the detection and differentiation of several types of BLs.

    In the case of RLs, 12 studies met the inclusion criteria for this review. This indicates that RLs detection is more challenging. However, the results obtained in some studies [ Table 2 ] were only slightly below the BDA values.

    Diagnosing Diabetic Eye Disease

    It should be noted that not only SE and SP figures are important; the number of images employed in each study must be taken into account. In the case of Fleming et al. However, it is noteworthy that the methods included in the competition could be directly compared using a common database and evaluation criteria.

    Several of the studies included in Table 3 focus on separating DR and healthy cases. The inclusion of a DR severity grading stage poses additional complexity to these systems since DR severity grades were not standardized and nonuniform severity scales were used. Although the results of the developed algorithms are promising, challenges still remain. Further work is necessary to improve the proposed CAD systems so they can efficiently reduce the workload of ophthalmologists. First, the proposed methods should be tested on larger databases to ensure that they are capable of preventing visual loss in DR patients in a cost-effective way.

    Although there are some publicly available databases designed for automatic retinal image analysis, algorithms should be tested in a larger datasets representative from screening scenarios. In addition, inter- and intra-observer variability should be addressed in studies related to DR lesions detection or DR screening software development. Finally, DR severity grading systems should be consistent with clinically approved DR severity scales and thus, consider the different signs of DR.

    Despite these difficulties; several research groups are working toward the improvement and validation of CAD systems to efficiently diagnose DR and determine the DR severity grade in a patient. The final goal would be to develop an automatic system for DR screening with enough accuracy to be incorporated in the daily clinical practice. National Center for Biotechnology Information , U. Journal List Indian J Ophthalmol v.

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    Indian J Ophthalmol. Roberto Hornero 1 Department T. Author information Article notes Copyright and License information Disclaimer. Correspondence to: Miss. E-mail: moc. Received Jul 30; Accepted Nov This article has been cited by other articles in PMC. Abstract Diabetic retinopathy DR is a disease with an increasing prevalence and the main cause of blindness among working-age population. Keywords: Automated analysis system, diabetic retinopathy, retinal image.

    Materials The methodological quality of published articles was evaluated, and the following inclusion criteria were defined. Segmentation of exudates EXs are lipoprotein intraretinal deposits due to vascular leakage. Open in a separate window. Region growing methods With these techniques, images are segmented using the spatial contiguity of gray levels.

    Thresholding methods With these methods, EXs identification was based on a global or adaptive gray level analysis.

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    Mathematical morphology methods The algorithms based on these methods employed morphological operators to detect structures with defined shapes. Classification methods These studies employed machine learning approaches to separate EX from non-EX regions, including additional types of bright lesions BLs , such as drusen and CWSs.

    Segmentation of red lesions microaneurysms and hemorrhages MAs are small saccular bulges in the walls of retinal capillary vessels. Region growing methods Fleming et al. Mathematical morphology methods A polynomial contrast enhancement operation, based on morphological reconstruction methods, was used by Walter et al.

    Wavelet-based methods The method proposed by Quellec et al. Hybrid methods A hybrid RL segmentation algorithm was developed by Niemeijer et al. Diabetic retinopathy screening systems The previously mentioned studies and the related algorithms have enabled different research groups to develop computer-aided diagnosis CAD systems for DR screening. Table 3 Comparison of automatic diabetic retinopathy screening systems.

    Discussion Early DR detection is important to slow down disease progression and avoid severe vision loss in diabetic patients. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. References 1.

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    Perceptions of diabetic retinopathy and screening procedures among diabetic people. Diabet Med. Screening for diabetic retinopathy. Ann Intern Med.

    Retinal Image Processing in MATLAB: Optic Disc Localization

    Stereo nonmydriatic digital-video color retinal imaging compared with early treatment diabetic retinopathy study seven standard field mm stereo color photos for determining level of diabetic retinopathy. Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs.

    Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

    Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. Br J Ophthalmol. Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Graph.