RIM-ONE: An open retinal image database for optic nerve evaluation

Fumero, Francisco; Alayón, Silvia; Sánchez, Jorge; Sigut, J.; González-Hernández, Marta

doi:10.1109/cbms.2011.5999143

Cited by 407 publications

(200 citation statements)

References 12 publications

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“…The proposed method is evaluated on publicly available RIM-ONE r3 database [26]. The RIM-ONE database is specifically designed for glaucoma diagnosis, not only for medical educational purposes, but also as an evaluation tool for designers of segmentation algorithms.…”

Section: Resultsmentioning

confidence: 99%

Geometrical Feature Extraction for Glaucoma Detection

Arulmary¹,

Victor²

2018

IJCA

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Glaucoma affects most of the eyes of the people which leads to blindness. Glaucoma harms the optic nerve cells that transmit graphic information to the brain. Hence it is important to detect glaucoma in eyes. Cup-to-Disc Ratio (CDR) is commonly used as an important parameter for glaucoma screening, involving segmentation of the optic cup on fundus images. A novel approach is proposed using the intensity values and size of the cup and disc. The proposed method uses the radius of the cup and disc for feature extraction. The features are classified using Support Vector Machine (SVM) classifier. The proposed method uses RIM-ONE dataset for evaluation. It achieves 99% specificity at 82% sensitivity with 0.863 AUC.

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Section: Resultsmentioning

confidence: 99%

Geometrical Feature Extraction for Glaucoma Detection

Arulmary¹,

Victor²

2018

IJCA

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“…Depth is recorded as intensities and registered manually with the reference stereo images to create depth ground truth for evaluation. Our glaucoma prediction approach is evaluated on a set of 100 images (T2) created using publicly available RIM-ONE R1, RIM-ONE R2 and RIM-ONE R3 datasets [11]. T2 consists of 53 normal images, and 47 glaucoma and glaucoma suspicious images.…”

Section: Resultsmentioning

confidence: 99%

A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images

Ramaswamy

Ram

Sivaprakasam

2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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“…An Open Retinal Image Database for Optic Nerve Evaluation (RIM-ONE) is a fundus image database that is related to glaucoma disease. I It consists of 169 ONH images, where the images are divided into several classes as follows: normal 118 images, early glaucoma 12 images, moderate glaucoma 14 images, deep glaucoma 14 images and ocular hypertension (OHT) 11 images [35,36]. In this study the RIM-ONE database is employed as it is considered precise gold standards of the ONH.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Grid Color Moment Features in Glaucoma Classification

Ghosh¹,

Sarkar²,

Ashour³

et al. 2015

ijacsa

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Abstract-Automated diagnosis of glaucoma disease is focused on the analysis of the retinal images to localize, perceive and evaluate the optic disc. Clinical decision support system (CDSS) is used for glaucoma classification in human eyes. This process depends mainly on the feature type that can be morphological or non-morphological. It is originated in the retinal image analysis technique that used color feature, texture features, extract structure, or contextual. This work proposes an empirical study on a narrative automated glaucoma diagnosis, classification system based on both Grid Color Moment method as a feature vector to extract the color features (nonmorphological) and neural network classifier. Consequently, these features are fed to the back propagation neural network (BPNN) classifier for automated diagnosis. The proposed system was tested using an open RIM-ONE database with accurate gold standards of the optic nerve head. This work classifies both normal and abnormal defected retina with glaucoma images. The experimental results achieved an accuracy of 87.47%. Thus, the proposed system can detect the early glaucoma stage with good accuracy.

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RIM-ONE: An open retinal image database for optic nerve evaluation

Cited by 407 publications

References 12 publications

Geometrical Feature Extraction for Glaucoma Detection

Geometrical Feature Extraction for Glaucoma Detection

A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images

Grid Color Moment Features in Glaucoma Classification

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