Accurate and Efficient Segmentation of Optic Disc and Optic Cup in Retinal Images Integrating Multi-View Information

Yuan, Guandou; Yu, Xiaoqing; Wu, Chengdong; Zhou, Wei; Wang, Xiaonan; Chen, Hao

doi:10.1109/access.2019.2946374

Cited by 23 publications

(6 citation statements)

References 36 publications

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“…TP + TN TP + TN + FP + FN (14) (b) Sensitivity : The TP Ratio to the total of True Positive (TP) and False Negative (FN) is Sensitivity. The following formula in Eq.…”

Section: Accuracy =mentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

David¹,

Selvi²,

Sivaprakash³

et al. 2022

Computers, Materials &Amp; Continua

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Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network (CNN) classification for classifying Glaucoma or Diabetes Retinopathy (DR). The detection of the boundary between the OC and the OD is performed by the SBEFCM, which is the latest weighted ellipse fitting model. The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here. There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels. The ascertaining of OC and OD boundary, which characterized many output factors for glaucoma detection, has been developed by Ensemble CNN classification, which includes detecting sensitivity, specificity, precision, and Area Under the receiver operating characteristic Curve (AUC) values accurately by an innovative SBEFCM. In terms of contrast, the proposed Ensemble CNN significantly outperformed the current methods.

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“…TP + TN TP + TN + FP + FN (14) (b) Sensitivity : The TP Ratio to the total of True Positive (TP) and False Negative (FN) is Sensitivity. The following formula in Eq.…”

Section: Accuracy =mentioning

confidence: 99%

“…Glaucoma is the second foremost reason for blindness. A user-friendly app called Yanbao was developed for premium-quality Glaucoma screening [13][14][15][16]. The critical problems inside the retinal imageries are supposed to be complicated blood vessel structure and acute intensified similarity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

David¹,

Selvi²,

Sivaprakash³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Ref. [52], and [53] utilized the same dataset, i.e., DRISHTI_GSI dataset, achieving an F1 score of 82.20% and 97.10% (maximum based on OD). Our proposed algorithm RF with feature descriptors HOG, CNN, and LBP is based on OD segmentation that consists of OC, giving an accuracy of 99% on testing data and 98.8% on cross-validation.…”

Section: Comparative Analysis With State-of-the-artmentioning

confidence: 99%

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

et al. 2021

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Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing <=99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

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“…Given a retinal image (see Figure 4(a)), we employ our previous proposed approach based on saliency detection and feature extraction techniques to locate optic disc [1]. The detected optic disc location is marked in black “+” (see Figure 4(b)).…”

Section: Preprocessingmentioning

confidence: 99%

“…Glaucoma is the second leading cause of blinding in modern times and approximately 80 million persons to be afflicted with glaucoma by the year 2020 [1, 2]. Since the lost capabilities of the optic nerve fibers caused by glaucoma cannot be recovered, early detection and timely treatment of glaucoma can be regarded as the most effective way for patients to slow down the procession of visual damage.…”

Section: Introductionmentioning

confidence: 99%

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Zhou

Yuan

et al. 2019

Computational and Mathematical Methods in Medicine

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Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

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Accurate and Efficient Segmentation of Optic Disc and Optic Cup in Retinal Images Integrating Multi-View Information

Cited by 23 publications

References 36 publications

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

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