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      Detection of Exudates and Microaneurysms in the Retina by Segmentation in Fundus Images Translated title: Detección de Exudados y Microaneurismas en la Retina por Segmentación en Imágenes de Fondo

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          Abstract

          ABSTRACT This article proposes two methodologies for the detection of lesions in the retina, which may indicate the presence of diabetic retinopathy (DR). Through the use of digital image processing techniques, it is possible to isolate the pixels that correspond to a lesion of RD, to achieve segmenting microaneurysms, the edges of the objects contained in the image are highlighted in order to detect the contours of the objects to select by size those that meet an area of 15 to 25 pixels in the case of 512x512 images and identify the objects as possible microaneurysms, while for the detection of exudates the green channel is selected to contrast the luminous objects in the retinography and from the conversion to gray scale, a histogram is graphed to identify the ideal threshold for the segmentation of the pixels that belong to the exudates at the end of the optical disk previously identified by a specialist. A confusion matrix supervised by an ophthalmologist was created to quantify the results obtained by the two methodologies, obtaining a specificity of 0.94 and a sensitivity of 0.97, values that are outstanding to proceed with the classification stage.

          Translated abstract

          RESUMEN Este artículo propone dos metodologías para la detección de lesiones en la retina, que pueden significar la presencia de retinopatía diabética (RD). Mediante el uso de técnicas de procesamiento de imágenes digitales se logra aislar los pixeles que corresponden a una lesión propia de RD, para lograr segmentar microaneurismas se resaltan los bordes de los objetos contenido en la imagen con la finalidad de detectar los contornos de los objetos para seleccionar por tamaño los que cumplan con un área de 15 a 25 pixeles en el caso de imágenes de 512x512 y se identifiquen los objetos como posibles microaneurismas, mientras que para la detección de exudados se selecciona el canal verde para contrastar los objetos luminosos en la retinografía y a partir de la conversión a escala de grises se grafica un histograma para identificar el umbral idóneo para la segmentación de los pixeles que pertenecen a los exudados al final eliminar el disco óptico previamente identificado por un especialista. Se creó una matriz de confusión supervisada por un oftalmólogo para cuantificar los resultados obtenidos por las dos metodologías obteniendo una especificidad del 0.94 y una sensibilidad del 0.97, unos valores que son sobresalientes para proceder con la etapa de clasificación.

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          FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

          The Messidor database, which contains hundreds of eye fundus images, has been publicly distributed since 2008. It was created by the Messidor project in order to evaluate automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing and maintaining such a database entails significant costs. By publicly sharing it, one hopes to bring a valuable resource to the public research community. However, the real interest and benefit of the research community is not easy to quantify. We analyse here the feedback on the Messidor database, after more than 6 years of diffusion. This analysis should apply to other similar research databases.
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            Automated microaneurysm detection using local contrast normalization and local vessel detection.

            Screening programs using retinal photography for the detection of diabetic eye disease are being introduced in the UK and elsewhere. Automatic grading of the images is being considered by health boards so that the human grading task is reduced. Microaneurysms (MAs) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy. This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina. Various methods for contrast normalization are compared. Best results were obtained with a method that uses the watershed transform to derive a region that contains no vessels or other lesions. Dots within vessels are handled successfully using a local vessel detection technique. Results are presented for detection of individual MAs and for detection of images containing MAs. Images containing MAs are detected with sensitivity 85.4% and specificity 83.1%.
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              Retinal vascular tree morphology: a semi-automatic quantification.

              A semi-automatic method to measure and quantify geometrical and topological properties of continuous vascular trees in clinical fundus images is described. Measurements are made from binary images obtained with a previously described segmentation process. The skeletons of the segmented trees are produced by thinning,ff branch and crossing points are identified and segments of the trees are labeled and stored as a chain code. The operator selects a tree to be measured and decides if it is an arterial or venous tree. An automatic process then measures the lengths, areas and angles of the individual segments of the tree. Geometrical data and the connectivity information between branches from continuous retinal vessel trees are tabulated. A number of geometrical properties and topological indexes are derived. Vessel diameters and branching angles are validated against manual measurements and several derived geometrical and topological properties are extracted from red-free fundus images of ten normotensive and ten age- and sex-matched hypertensive subjects and compared with previously reported results.
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                Author and article information

                Journal
                rmib
                Revista mexicana de ingeniería biomédica
                Rev. mex. ing. bioméd
                Sociedad Mexicana de Ingeniería Biomédica (México, Distrito Federal, Mexico )
                0188-9532
                2395-9126
                August 2021
                : 42
                : 2
                : 1136
                Affiliations
                [1] Guerrero orgnameInstituto Tecnológico de Acapulco Mexico
                [2] Guerrero orgnameInstituto Tecnológico de Chilpancingo Mexico
                Article
                S0188-95322021000200105 S0188-9532(21)04200200105
                10.17488/rmib.42.2.6
                49545ee4-dfcc-447f-a9ff-07a37b97b198

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 01 March 2021
                : 13 November 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 24, Pages: 0
                Product

                SciELO Mexico

                Categories
                Research articles

                Microaneurismas, Procesamiento de imágenes,Diabetic Retinopathy,Exudates,Microaneurysms,Image Processing,Segmentation,Retinopatía Diabética,Exudados,Segmentación

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