Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

Joshi, Gopal Datt; Sivaswamy, Jayanthi; Krishnadas, Subbaiah Ramasamy

doi:10.1109/tmi.2011.2106509

Cited by 375 publications

(235 citation statements)

References 39 publications

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“…The benchmark results are for the training set and are taken from [12]. (b)Mean CDR estimation error compared to other methods (a) Method/Organ optic cup optic disc CSR 0.86 ± 0.06 0.95 ± 0.02 SR 0.85 ± 0.08 0.95 ± 0.02 [6] 0.74 ± 0.20 0.96 ± 0.02 [13] 0.77 ± 0.17 - [14] 0.80 ± 0.18 -(b) CDR estimation error Method error CSR 0.08 ± 0.1 PR 0.09 ± 0.12 SR 0.12 ± 0.1 Fig. 1: Example outputs of our proposed OD-OC segmentation method.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Sedai

Roy

Mahapatra

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Accurate segmentation of optic cup and disc in retinal fundus images is required to derive the cup-to-disc ratio (CDR) parameter which is the main indicator for Glaucoma assessment. In this paper, we propose a coupled regression method for accurate segmentation of optic cup and disc in retinal colour fundus image. The proposed coupled regression framework consists of a parameter regressor which directly predicts CDR from a given image, as well as an ensemble shape regressor which iteratively estimates the OD-OC boundary by taking into account the CDR estimated by the parameter regressor. The parameter regressor and the shape regressor are then coupled together within a feedback loop so that estimation of one reinforces the other. Both parameter regressor and the ensemble shape regressor are modeled using Boosted Regression Trees. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrates high segmentation accuracy. A comparative study shows that our proposed method outperforms state of the art methods for cup segmentation.

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Section: Methodsmentioning

confidence: 99%

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Sedai

Roy

Mahapatra

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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show abstract

“…The assessment of glaucoma using optic disc and optic cup segmentation from monocular colour retinal images is presented in [3]. In multidimensional feature space, the information of local image is integrated around each point of interest for optic disc segmentation.…”

Section: Introductionmentioning

confidence: 99%

Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image

Maharaja¹,

Shaby²

2017

IJMSR

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Abstract:Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. There is a need to diagnose glaucoma accurately with low cost. In this paper, a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT) is proposed. The EWT is used to decompose the image and Gray Level Concurrence Matrix (GLCM) features are obtained from decomposed EWT components. Then, these features are used for the classification of normal and glaucoma images using Neural Network (NN) classifier. The evaluation of the system is carried on using 46 generated images from that 20 images are used for training purpose and 26 images are used for testing purpose. In this the classification rate of the proposed system is satisfied. Overall accuracy of the proposed system is 96%.

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“…In some cases, in the OC there is an occlusion problem from the blood vessels which can be overcome by smoothing the boundary by ellipse fitting techniques. However, in some purpose, the region or area of the OC can be detected by a region of support concept 10 in order to derive the desired OC boundary. The texture and intensity features 11 can be used as well.…”

Section: Introductionmentioning

confidence: 99%

An automatic screening method for primary open-angle glaucoma assessment using binary and multi-class support vector machines

Vejjanugraha¹,

Kongprawechnon²,

Kondo³

et al. 2017

ScienceAsia

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Glaucoma is a chronic progressive eye condition leading to permanent visual loss. An automatic screening system is necessary to detect primary open-angle glaucoma because it is an insidious disease appearing without symptoms or early warning signs. This work introduces an automatic screening technique to diagnose glaucoma using a support vector machine (SVM). Two case studies are investigated: binary-stage and multi-stage classification of glaucoma. First, there is a comparison of the performance of the hard threshold-based approach to the supervised learning approach using an SVM. Image segmentation techniques are performed to detect important features: the actual sizes of the optic cup and optic disc in vertical and horizontal directions. SVMs with a linear kernel function are used to generate the classifier model, and the results show that using threshold-based classification is inadequate to screen glaucoma. In a second case study, an SVM is applied to develop the classification algorithm focused more on the detection of the glaucoma suspect stage, which is an intermediate stage between the healthy and glaucoma stages. A polynomial kernel function is used to implement the classification model. The unbalanced decision tree (UDT) and one-versus-the-rest (OVR) techniques are combined in the models in order to overcome the limitations of an SVM. Finally, the combination of an SVM with both UDT and OVR techniques yields a reliable result with respect to belonging classes at 99.4%.

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Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

Cited by 375 publications

References 39 publications

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image

An automatic screening method for primary open-angle glaucoma assessment using binary and multi-class support vector machines

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