Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis

Xu, Yanwu; Xu, Dong; Lin, Stephen; Liu, Jiang; Cheng, Jun; Cheung, Carol Y.; Aung, Tin; Wong, Tien Yin

doi:10.1007/978-3-642-23626-6_1

Cited by 44 publications

(62 citation statements)

References 9 publications

(14 reference statements)

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“…We compare our algorithm to state-of-the-art reconstruction-based [7], pixel [1], sliding window [2] and superpixel [3][18] based methods. In addition, we compare our system against the current clinical standard for glaucoma detection using intra-ocular pressure (IOP) and to CDR values from expert graders.…”

Section: Methodsmentioning

confidence: 99%

Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

Chen

Yan

et al. 2015

Lecture Notes in Computer Science

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103

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Abstract. Glaucoma is a chronic and irreversible eye disease in which the optic nerve is progressively damaged, leading to deterioration in vision and quality of life. In this paper, we present an Automatic feature Learning for glAucoma Detection based on Deep LearnINg (ALADDIN), with deep convolutional neural network (CNN) for feature learning. Different from the traditional convolutional layer that uses linear filters followed by a nonlinear activation function to scan the input, the adopted network embeds micro neural networks (multilayer perceptron) with more complex structures to abstract the data within the receptive field. Moreover, a contextualizing deep learning structure is proposed in order to obtain a hierarchical representation of fundus images to discriminate between glaucoma and non-glaucoma pattern, where the network takes the outputs from other CNN as the context information to boost the performance. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.838 and 0.898 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma diagnosis.

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Section: Methodsmentioning

confidence: 99%

Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

Chen

Yan

et al. 2015

Lecture Notes in Computer Science

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103

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“…This allows for richer classification features than pixel-based methods, and faster processing than sliding window techniques with comparable accuracy. In addition, in contrast to the supervised approaches [7], our method requires no training procedure. …”

Section: Introductionmentioning

confidence: 89%

“…[4] describes using variational level-set methods to accommodate to the boundary of the optic cup. Recently, [7] introduced a machine learning framework based on sliding windows for optic cup segmentation.…”

Section: Introductionmentioning

confidence: 99%

Domain prior based superpixel propagation for optic cup localization

Tan

Liu

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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In this paper, we present an unsupervised framework using domain priors extracted from the primary structures of the optic nerve head for automated optic cup localization. Our approach provides 3 major contributions. First, we identify a new domain prior, optic cup origin. This prior is derived from the physiological understanding that the central retinal vessels traces its origin from the optic cup before extending to the rest of the retinal. Second, we propose extracting the features of the optic nerve head from superpixels, which are obtained from low-level grouping and have more natural and descriptive features than pixel based techniques. Third, the domain knowledge comprising of optic cup origin and cup pallor, and the extracted features from superpixels are then used to drive a similarity-based label propagation and refinement scheme for the optic cup localization. Our approach was validated on a clinical online dataset, ORIGA-light, of 650 population-based images. Overall, our approach is able to achieve a 32.2% nonoverlap ratio (m 1 ), a 33.8% relative absolute area difference (m 2 ) and a 10.6% absolute CDR error (δ).

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“…Methods like threshold [7], r-bend [7], ASM [18], regression [16] are well compared in the latest papers like [2], [11] and so on. So we conducted experiments to compare with the superpixel based method [2] and the spatially weighted fuzzy c-means(SWFCM) based method [11].…”

Section: Comparision With Other Methodsmentioning

confidence: 99%

Optic Cup Segmentation Using Large Pixel Patch Based CNNs

Guo

Zou

Chen

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Optic cup(OC) segmentation on color fundus image is essential for the calculation of cup-to-disk ratio and fundus morphological analysis, which are very important references in the diagnosis of glaucoma. In this paper we proposed an OC segmentation method using convolutional neural networks(CNNs) to learn from big size patch belong to each pixel. The segmentation result is achieved by classification of each pixel patch and postprocessing. With large pixel patch, the network could learn more global information around each pixel and make a better judgement during classification. We tested this method on public dataset Drishti-GS and achieved average F-Score of 93.73% and average overlapping error of 12.25%, which is better than state-of-the-art algorithms. This method could be used for fundus morphological analysis, and could also be employed to other medical image segmentation works which the boundary of the target area is fuzzy.

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Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis

Cited by 44 publications

References 9 publications

Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

Domain prior based superpixel propagation for optic cup localization

Optic Cup Segmentation Using Large Pixel Patch Based CNNs

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