Optic nerve head segmentation in multimodal retinal images

Chrástek, Radim; Niemann, Heinrich; Kubecka, Libor; Jan, Jiří; Derhartunian, V.; Michelson, Georg

doi:10.1117/12.594492

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The algorithm obtained 90% accuracy. Furthermore, canny edge detection and Hough transformation have been used for the localization of OD [11].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the remaining components have been computed. The Hessian matrix determinant is represented in Equation (11).…”

Section: Surf As Feature Descriptormentioning

confidence: 99%

“…Electronics 2022, 11,26 proposed method, the objective classes are two, i.e., d most representative features, it is necessary to improv tive class and features. Suppose i and j are points, th and j is represented as Ώ in Equation ( 12).…”

Section: Classificationmentioning

confidence: 99%

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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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“…The algorithm obtained 90% accuracy. Furthermore, canny edge detection and Hough transformation have been used for the localization of OD [11].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the remaining components have been computed. The Hessian matrix determinant is represented in Equation (11).…”

Section: Surf As Feature Descriptormentioning

confidence: 99%