Retinal image analysis: Concepts, applications and potential

Patton, Niall; Aslam, Tariq; MacGillivray, Tom; Deary, Ian J.; Dhillon, Baljean; Eikelboom, Robert H.; Kanagasingam, Yogesan; Constable, Ian J.

doi:10.1016/j.preteyeres.2005.07.001

Cited by 536 publications

(216 citation statements)

References 183 publications

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“…It has been used as explore tools to get more knowledge about the connection between the retinal microvasculature and cardiovascular disease. Niall Patton et al [14] give explanation about the principles based upon which retinal digital image analysis. The author said that the present technique automatically identify landmark features of the fundus, like optic disc, fovea and blood vessels.…”

Section: Survey On Retinal Image Techniquesmentioning

confidence: 99%

A Comparative Analysis of Retinal Blood Vessels Approaches

Karthika¹,

Marimuthu²

2013

IJCA

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Image processing is intended for which the input is an image, like photograph or some other videos the output of may be whichever an image or, a set of characteristics or limitations consistent to the image. Retinal images has numerous qualitative procedures can be used in more applications, such as ocular fundus operations with human recognition. Likewise, it plays important roles in detection of a few diseases in premature stages, such as diabetes, which can be performed by evaluation of the states of retinal blood vessels. Inherent characteristics of retinal images make the blood vessel detection process tricky. This survey presents an analysis of several algorithms proposed by various authors to detect the retinal blood vessels effectively. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. Then, in order to improve the performance of the best approach, a novel approach is been proposed in this paper.

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Section: Survey On Retinal Image Techniquesmentioning

confidence: 99%

A Comparative Analysis of Retinal Blood Vessels Approaches

Karthika¹,

Marimuthu²

2013

IJCA

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“…The global histogram of the three image classes is computed using the global vessel density 2 The zero mean and unit variance is calculated for each feature across all the training images 21) The first number represents the average quality score (i.e. The posterior probability for the image to belong to "Good Quality") for all the images tested in a class.…”

Section: B Global Vessel Density Distributionmentioning

confidence: 99%

“…In the recent years a substantial effort of the research community went towards the creation of automatic or semi-automatic systems to detect various type of retinopathy. The results obtained are promising but they are strongly dependent on the initial image quality [2].…”

Section: Introductionmentioning

confidence: 97%

Elliptical local vessel density: A fast and robust quality metric for retinal images

Giancardo¹,

Abràmoff

Chaum

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patient's vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques.

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“…Diabetic retinopathy particularly is a major sight-threatening complication caused by diabetes mellitus. Diabetes harmfully affects the retina (diabetic retinopathy), as well as the kidneys (diabetic nephropathy) and the nervous system (diabetic neuropathy) as stated by Patton et al (2006) and Allam et al (2015). Also, WHO (2016) reported that 2.6% of global blindness could be attributed to diabetic retinopathy, in which the number of people with diabetes had risen from 108 million in 1980 to 422 million in 2014.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

Ghalwash¹,

Youssif²,

Allam³

2017

Journal of Computer Science

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This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised kmeans algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fullyautomated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

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Retinal image analysis: Concepts, applications and potential

Cited by 536 publications

References 183 publications

A Comparative Analysis of Retinal Blood Vessels Approaches

A Comparative Analysis of Retinal Blood Vessels Approaches

Elliptical local vessel density: A fast and robust quality metric for retinal images

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

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