Feedback on a Publicly Distributed Image Database: The Messidor Database

Decencière, Étienne; Zhang, Xiwei; Cazuguel, Guy; Laÿ, Bruno; Cochener, Béatrice; Trone, Caroline; Gain, Philippe; Ordóñez-Varela, John-Richard; Massin, Pascale; Erginay, Ali; Charton, B; Klein, Jean-Claude

doi:10.5566/ias.1155

Cited by 996 publications

(431 citation statements)

References 7 publications

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“…Diabetic Retinopathy (DR) Debrecen Dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not [18,19]. It contains 19 features (a Euclidean distance of the center of the macula and the center of the optic disc, the binary result of the AM/FM-based classification, etc.)…”

Section: Datasetsmentioning

confidence: 99%

Anomaly Detection in Big Data based on Clustering

Alguliyev

Alıguliyev

Sukhostat

2017

Stat., optim. inf. comput.

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Selection of the right tool for anomaly (outlier) detection in Big data is an urgent task. In this paper algorithms for data clustering and outlier detection that take into account the compactness and separation of clusters are provided. We consider the features of their use in this capacity. Numerical experiments on real data of different sizes demonstrate the effectiveness of the proposed algorithms.

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Section: Datasetsmentioning

confidence: 99%

Anomaly Detection in Big Data based on Clustering

Alguliyev

Alıguliyev

Sukhostat

2017

Stat., optim. inf. comput.

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“…In order to evaluate the performance of the proposed algorithm we have relied on a subset of the MESSIDOR dataset [36] . A set of 20 images were chosen from this dataset to cover a wide range of retinopathy as shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Microaneurysm detection in retinal images using an ensemble classifier

Habib

Welikala

Hoppe

et al. 2016

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Abstract-Diabetic Retinopathy (DR) is one of the leading causes of blindness amongst the working age population. The presence of microaneurysms (MA) in retinal images is a pathognomonic sign of DR. In this work we have presented a novel combination of algorithms applied to a public dataset for automated detection of MA in colour fundus images of the retina. The proposed technique first detects an initial set of candidates using a Gaussian Matched filter and then classifies the initial set of candidates in order to reduce the number of false positives. A Random Forest ensemble classifier using a set of 79 features (the most common features used within literature) was used for classification. Our proposed algorithm was evaluated on a subset of 20 images from the MESSIDOR dataset. We show that the use of the Random Forest classifier with the 79 features improves the sensitivity of the detection, compared to using a K-Nearest Neighbours classifier that has been proposed in other techniques. In addition, the Random Forest is capable of ranking features according to their importance. We have ranked the 79 features according to their importance. This ranking provides an insight into the most important features that are necessary for discriminating true MA candidates from spurious objects. Eccentricity, aspect ratio and moments are found to be among the important features.

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“…These stages are explained as follows. An existing dataset (called MESSIDOR) [17] is used in this study for training and evaluation purposes because it is a fairly large dataset and it is labelled. It comprises 1200 images classified as either normal (no DME), Non-CSME or CSME.…”

Section: Methodsmentioning

confidence: 99%

Diabetic Macular Edema Grading Based on Deep Neural Networks

Al-Bander

Al-Nuaimy

Al-Taee

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach.

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Feedback on a Publicly Distributed Image Database: The Messidor Database

Cited by 996 publications

References 7 publications

Anomaly Detection in Big Data based on Clustering

Anomaly Detection in Big Data based on Clustering

Microaneurysm detection in retinal images using an ensemble classifier

Diabetic Macular Edema Grading Based on Deep Neural Networks

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