Glaucoma risk index:Automated glaucoma detection from color fundus images

Bock, Rüdiger; Meier, Jörg; Nyúl, László G.; Hornegger, Joachim; Michelson, Georg

doi:10.1016/j.media.2009.12.006

Cited by 303 publications

(176 citation statements)

References 51 publications

(53 reference statements)

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“…In particular in ophthalmology photos of the eye background are used by medical experts to diagnose and document diseases like glaucoma or diabetic retinopathy. In addition the images are commonly further evaluated by automatic software tools to support the diagnosis [1][2][3].…”

Section: Motivationmentioning

confidence: 99%

Automated quality assessment of retinal fundus photos

Paulus¹,

Meier²,

Bock³

et al. 2010

Int J CARS

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Section: Motivationmentioning

confidence: 99%

Automated quality assessment of retinal fundus photos

Paulus¹,

Meier²,

Bock³

et al. 2010

Int J CARS

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“…Glaucoma Risk Index (GRI) was described by Bock et al (2010) to assess glaucoma. The accuracy of 80% has been achieved in a 5-fold cross-validation setup and the GRI gains an Area Under Convergence (AUC) of 88%.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Karthikeyan¹

2014

American Journal of Applied Sciences

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Glaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the preprocessed image as textural features reflects physiological changes in the fundus images. Second order textural features are extracted for different quantization levels namely 8, 16, 32, 64, 128 and 256 in four orientations viz 0, 45, 90 and 135° for various distances. Extracted features are selected using Sequential Forward Floating Selection (SFFS) technique.The selected features are fed to Back Propagation Network (BPN) for classification as normal and abnormal images. The proposed computer aided diagnostic system achieved 96% sensitivity, 94% specificity, 95% accuracy and can be used for screening purposes. In this study, the analysis of gray levels have shown their significance in the classification of glaucoma.

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“…The NFLDs appear as dark curved bands extending radically from an ONH on the spherical surface and developed different kinds of classification algorithm to detect the Glaucoma disease. "Rudiger Bock [5]" proposed a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. In this specific preprocessing and different generic feature types are compressed by an appearance-based dimension reduction technique.…”

Section: Literature Surveymentioning

confidence: 99%

Glaucoma and Diabetic Retinopathy Diagnosis using Image Mining

Panse¹,

Ghorpade²,

Jethani³

2015

IJCA

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In today's world human are affected by various diseases which lead to damage of some or the other body part which degrades their working speed. Eye diseases are one of the factors, which include vision loss due to glaucoma and diabetic retinopathy. Glaucoma damages the optic nerve of the eye. DR cause changes in eye damage the blood vessel. Image will undergo a standard method of applying image processing which include image acquisition, pre-processing, feature extraction followed by exact identification of disease by means of a classifier. Various classifiers are available in data mining that have been used for classification in different areas. We will use SVM for classification of the retinal images into category of Normal, DR and Glaucoma. The Overall classification rate of the proposed system will give the better efficiency and accuracy of identifying the disease with respect to existing systems.

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Glaucoma risk index:Automated glaucoma detection from color fundus images

Cited by 303 publications

References 51 publications

Automated quality assessment of retinal fundus photos

Automated quality assessment of retinal fundus photos

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Glaucoma and Diabetic Retinopathy Diagnosis using Image Mining

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