A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

Welfer, Daniel; Scharcanski, Jacob; Marinho, Diane Ruschel

doi:10.1016/j.compmedimag.2009.10.001

Cited by 153 publications

(79 citation statements)

References 17 publications

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“…Welfer et al [5] applied mathematical morphology using the perceptually uniform Luv of colour space for lightness L, where the variations of intensity in the L band were slightly less than in the retinal fundus image. They also applied morphological operations based on top-hat techniques, local minima and thresholding techniques to detect the exudate regions.…”

Section: Exudates Optic Discmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Omar

Hossain

Zhang

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract-Diabetic retinopathy is considered in terms of the presence of exudates which cause vision loss in the areas affected. This study targets the development of an intelligent mobile-based automatic diagnosis integrated with a microscopic lens to identify retinal diseases at initial stage at any time or place. Exudate detection is a significant step in order obtaining an early diagnosis of diabetic retinopathy, and if they are segmented accurately, laser treatment can be applied effectively. Consequently, precise segmentation is the fundamental step in exudate extraction. This paper proposes a technique for exudate segmentation in colour retinal images using morphological operations. In this method, after pre-processing, the optic disc and blood vessels are isolated from the retinal image. Exudates are then segmented by a combination of morphological operations such as the modified regionprops function and a reconstruction technique. The proposed technique is verified against the DIARETDB1 database and achieves 85.39% sensitivity. The proposed technique achieves better exudate detection results in terms of sensitivity than other recent methods reported in the literature. In future work, our system will be deployed to a mobile platform to allow efficient and instant diagnosis.

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Section: Exudates Optic Discmentioning

confidence: 99%

“…The optic disc is the point of exit of the optic nerve and the fovea describes the centre of the macula of the retina, and this region gives most acute. The third section consists of the exudates as explained previously [5].…”

Section: Introductionmentioning

confidence: 99%

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Omar

Hossain

Zhang

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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“…Fuzzy C-means clustering was incorporated with spatial neighbourhood information [4]. Mathematical morphologic methods have been used [5,6]. Neural Networks and Support-vector machines were used in [7].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Lee¹,

Zee²,

Li³

2013

JBiSE

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Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

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“…Taking into account only one parameter is not possible to establish the goodness of that method. The algorithm introduced by Welfer et al (2010) provides interesting results. However, analysing together the three parameters of Table 6.14, is possible to observe that our method outperforms the Welfers' one.…”

Section: Performance Of Machine Learning Algorithmsmentioning

confidence: 99%

“…In the literature, the most common procedure is to segment these lesions using different methods (Ghafourian and Pourreza, 2012;Walter et al, 2002;Welfer et al, 2010). These approaches present a high false-positive rate at pixel level.…”

Section: Introductionmentioning

confidence: 99%

Fundus image analysis for automatic screening of ophthalmic pathologies.

Granero¹

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AcknowledgmentsEste documento es el resultado de cuatro años de esfuerzo y dedicación que han propiciado mi evolución tanto a nivel profesional como personal. El principal motivo de dicho desarrollo han sido todas esas personas que han formado parte de este camino o que de alguna forma se han cruzado en el mismo. A todos ellos quiero dedicarles unas palabras de agradecimiento.En primer lugar, quiero agradecer a Valery, ella es la responsable de que yo esté ahora mismo escribiendo estas líneas. Me brindó la oportunidad de mi vida hace ya más de cinco años y me ha ido formando tanto en la vertiente investigadora como docente de la misma forma que una madre educa e inculca los valores esenciales a su hijo. Gracias por todo Valery, juntos cerramos una etapa clave en mi formación y juntos abrimos una nueva cargada de ilusión. Espero poder estar siempre a tu lado.También quiero agradecer todo el apoyo, energía y momentos divertidos que me han brindado mis compañeros de laboratorio. Gracias Sandra por todo lo que me has enseñado desde el primer momento en el que llegué hasta el día de hoy, no sé qué haría yo sin ti en esa sala. Gracias Fer, Félix, Andrés, Reinier, Ángel, Gabri, Fran P., Rober, Jorge y Adri por todos los consejos aportados y por amenizar como sólo vosotros sabéis mi día a día. También quiero recordar y agradecer a los que decidieron emprender otro rumbo: Javi, Conchi, Pablo, Eliseo, Fran M. y Miguel.De igual forma quiero mostrar mi gratitud hacia varias personas que han colaborado de una u otra forma en este trabajo. A Jesús Angulo, Débora Gil y Ramón Baldrich por su supervisión del trabajo desarrollado en las estancias llevadas a cabo durante esta etapa. A Kjersti Engan por aportar sus conocimientos sobre Dictionary Learning & Sparse Representation que han culminado en diversas publicaciones. A Rafael Molina por permitirme adentrarme en el fascinante mundo de los Procesos Gaussianos y sus múltiples aplicaciones.Gracias a mi madre Jacqueline, mi hermano Fran, mis abuelos Mª Carmen y Paco, mi padre Luis y Eva que siempre me han brindado su apoyo incondicional. Como bien sabéis sois fundamentales para mí, por ello ésta tesis también va dedicada a vosotros. No me puedo olvidar de Rosa que ocupa una parte grande de mi corazón. Os quiero.A Duke y Tom por todas esas horas de compañía desinteresada que me han ofrecido durante sus vidas y en especial en la época de redacción de este manuscrito.Por último, quiero dedicar especialmente este trabajo al motor de mi vida, a la persona que tiene que lidiar conmigo en los momentos más difíciles, a la persona que siempre me acaba sacando una sonrisa, a la persona que me enseña día tras día lo que es amor, fuerza, lucha, perseverancia e infinidad de cualidades que la definen. Gracias por todo cariño, sin tu apoyo estoy seguro de que esto no hubiera sido posible. Durante estos 10 años hemos abierto y cerrado muchas etapas juntos pero lo más ilusionante son todas aquellas que nos quedan por vivir. vi AbstractIn last years, the number of blindness cases has been significan...

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A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

Cited by 153 publications

References 17 publications

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Fundus image analysis for automatic screening of ophthalmic pathologies.

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