Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Allam, Ali Mohamed Nabil; Youssif, Aliaa A. A.; Ghalwash, Atef Z.

doi:10.5565/rev/elcvia.680

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…The entropy of different patch images obtained by the sliding window technique was analyzed using recursive steps in their study [6]. Allam et al focused on this subject with three categories: (a) using the size, color, and brightness characteristics (b) forecasting the convergence point of the vessels (c) template matching [7]. Lalonde detected OD by template matching based on pyramidal decomposition and Hausdorff in low-resolution fundus images [12].…”

Section: Literature Reviewmentioning

confidence: 99%

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Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images

Akyol

Şen

2021

SN Appl. Sci.

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Detection of the optic disc which has similar brightness with the hard and soft exudate lesions seen in the early stage of diabetic retinopathy is very difficult due to different light conditions and contrast values. Automatic detection of these lesions by expert systems in the medical field is very important. In this context, we propose a new approach based on the analysis of color spaces, keypoint detectors, and texture for retinal fundus images. If the keypoint information is contained within the actual optic disc region, this is an important consideration for the automated detection of the optic disc. This study can be divided into five sections, respectively, image preprocessing, image processing, keypoint detection, texture analysis, and performance evaluation. The analyses of patch images compatible with the keypoints obtained from the Red–Green–Blue (RGB) image and its color channels were carried out. The performance of the study was validated on the Digital Retinal Images for Vessel Extraction public dataset. According to the results, Local Binary Pattern texture analysis performed in region of interest around keypoints detected by different keypoint detectors presented good performance in RGB and green channel images.

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Section: Literature Reviewmentioning

confidence: 99%

“…The detection of glaucoma eye disease is related to tracing the changes in the optic cup. This region must be detected in the retinal image [6] due to it enables ophthalmologists to mitigate the effects of these diseases [7].…”

Section: Introductionmentioning

confidence: 99%

Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images

Akyol

Şen

2021

SN Appl. Sci.

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“…It is the brightest part of a healthy eye and is seen as a bright yellowish or white region in a fundus image [7]. Exudates have similar values, so it is important to eliminate optic disk (OD) from the retinal image; the removal of OD is necessary.…”

Section: B the Detection Of Optic Diskmentioning

confidence: 99%

Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

Çifçi¹

2021

Preprint

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The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

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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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Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Cited by 10 publications

References 53 publications

Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images

Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images

Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

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

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