Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom H.

doi:10.1016/j.compmedimag.2008.08.009

Cited by 371 publications

(225 citation statements)

References 13 publications

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“…In our results, we compare our two techniques described in this paper with our implementation of [6] and [2]. In all instances, we evaluate the performance of the lesion segmentation algorithms on a lesion by lesion basis for each image.…”

Section: Resultsmentioning

confidence: 99%

“…In order to reduce the problem of noise we have developed a new way to normalise the fundus images. We directly compare our method with an implementation of a morphology based technique [6] and another thresholding based technique [2]. We selected the techniques that were tested with the largest datasets in the respective class.…”

Section: Introductionmentioning

confidence: 99%

“…Morphology methods employ greyscale morphological operators to identify all structures with predictable shapes (such as vessels). These structures are removed from the image so that exudates can be identified [5,6]. Classification methods build a feature vector for each pixel or pixel cluster, which are then classified by employing a machine learning approach into exudates or not exudates [7,8,9] or additional types of bright lesions [10,11] (such as drusen and cotton wool spots).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic retina exudates segmentation without a manually labelled training set

Giancardo¹,

Mériaudeau²,

Karnowski³

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, two new methods for the detection of exudates are presented. The methods do not require a lesion training set so the need to ground-truth data is avoided with significant time savings and independence from human error. We evaluate our algorithm with a new publicly available dataset from various ethnic groups and levels of DME. Also, we compare our results with two recent exudate segmentation algorithms on the same dataset. In all of our tests, our algorithms are either outperforming or in line with existing methods. Additionally, the computational time is one order of magnitude less than similar methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic retina exudates segmentation without a manually labelled training set

Giancardo¹,

Mériaudeau²,

Karnowski³

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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show abstract

“…Akara Sopharak et al [1] uses an automated method to detect exudates from lowcontrast fundus images of patients with non-dilated pupils using a FCM is proposed. Preprocessing using Contrast enhancement is applied for four features,ie, intensity, standard deviation, hue and als-o a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Exudative Maculopathy from Retinal Fundus Images

Sreedevi¹,

Menon²

2014

IJCA

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Diabetic retinopathy is a group of eye problems caused due to diabetes. Longer time a person has diabetes; the higher is his or her chances of developing diabetic retinopathy. One of the stages of diabetic retinopathy is Exudative Maculopathy. Spontaneous progression and regression of exudates causes Exudative Maculopathy.This work focuses on extraction of exudates from retinal fundus images using image processing techniques.

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“…It occurs when the layer of nerve fiber is clogged because of small arteries choked. Soft exudates are also known as cotton-wool spots [5]. Soft exudates are mostly confined with micro-aneurysm and vessels that are hyper-permeable.…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy

Elibol

Ergin

2016

ijisae

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Diabetes affects the capillary vessels in retina and causes vision loss. This disorder of retina due to diabetes is named as Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a publicly available database (DiaraetDB1) via detecting the symptoms of this disease. Time-domain features are extracted and selected to classify a fundus image. Fisher's Linear Discriminant Analysis (FLDA), Linear Bayes Normal Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as the classification methods in the experimental benchmarking. The recognition accuracies are obtained using all features (68 features) and selected features separately. k-NN is observed as the best classification method for without feature selection case and it gives averagely 92.22% accuracy. For feature selection case, LDC gives the best average accuracy as 92.45% with maximum 7 carefully chosen features.

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Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

Cited by 371 publications

References 13 publications

Automatic retina exudates segmentation without a manually labelled training set

Automatic retina exudates segmentation without a manually labelled training set

Detection of Exudative Maculopathy from Retinal Fundus Images

The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy

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