Feature Extraction from Optic Disc and Cup Boundary Lines in Fundus Images Based on ISNT Rule for Glaucoma Diagnosis

2015 ◽  
Vol 5 (8) ◽  
pp. 1833-1838 ◽  
Author(s):  
Yongli Xu ◽  
Xin Jia ◽  
Man Hu ◽  
Xu Sun
Keyword(s):  
Feature Extraction ◽  
Optic Disc ◽  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Isnt Rule ◽  
Boundary Lines

scholarly journals Automated Segmentation of Optic Disc and Cup in Color Fundus Images

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Vijay M Mane
Keyword(s):  
Optic Disc ◽  
Disease Diagnosis ◽  
Eye Disease ◽  
Automated Segmentation ◽  
Fundus Images ◽  
Average Sensitivity ◽  
Watershed Transformation ◽  
Glaucoma Diagnosis ◽  
Optic Cup ◽  
Color Fundus Images

An automatic Optic disc and Optic cup detection technique which is an important step in developing systems for computer-aided eye disease diagnosis is presented in this paper. This paper presents an algorithm for localization and segmentation of optic disc from digital retinal images. OD localization is achieved by circular Hough transform using morphological preprocessing and segmentation is achieved by watershed transformation. Optic cup segmentation is achieved by marker controlled watershed transformation. The optic disc to cup ratio (CDR) is calculated which is an important parameter for glaucoma diagnosis. The presented algorithm is evaluated against publically available DRIVE dataset. The presented methodology achieved 88% average sensitivity and 80% average overlap. The average CDR detected is 0.1983.

scholarly journals Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †

2019 ◽  
Vol 9 (15) ◽  
pp. 3064 ◽  
Author(s):  
Mijung Kim ◽  
Jong Chul Han ◽  
Seung Hyup Hyun ◽  
Olivier Janssens ◽  
Sofie Van Hoecke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Optic Disc ◽  
Medical Images ◽  
Computer Aided Diagnosis ◽  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Computer Aided ◽  
Different Types ◽  
Aided Diagnosis

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.

scholarly journals Computer-Aided Glaucoma Diagnosis Using Stochastic Watershed Transformation on Single Fundus Images

2019 ◽  
Vol 9 (6) ◽  
pp. 1057-1065
Author(s):  
Andres Diaz-Pinto ◽  
Sandra Morales ◽  
Valery Naranjo ◽  
Amparo Navea
Keyword(s):  
Fundus Images ◽  
Colour Space ◽  
Clinical Indicators ◽  
Watershed Transformation ◽  
Glaucoma Diagnosis ◽  
Specificity And Sensitivity ◽  
Initial Symptoms ◽  
Private And Public ◽  
Retinal Fundus ◽  
Isnt Rule

Glaucoma is a chronic eye disease and one of the major causes of permanent blindness. Since it does not show initial symptoms, early diagnosis is important to limit its progression. This paper presents an automatic optic nerve characterization algorithm for glaucoma diagnosis based only on retinal fundus images. For optic cup segmentation, we used a new method based on the stochastic watershed transformation applied on the YIQ colour space to extract clinical indicators such as the Cup/Disc ratio, the area Cup/Disc ratio and the ISNT rule. Afterwards, an assessment between normal and glaucomatous fundus images is performed. The proposed algorithm was evaluated on 6 different (private and public) databases containing 723 images (377 normal and 346 glaucomatous images) which achieved a specificity and sensitivity of 0.674 and 0.675, respectively. Moreover, an F-score of 0.770 was obtained when evaluating this method on the publicly available database Drishti-GS1. A comparison of the proposed work with other state-of-the-art methods demonstrates the robustness of the proposed algorithm; because it was tested using images from different databases with high variability, which is a common issue in this area. Additional comparisons with existing works for cup segmentation, that use the publicly available database Drishti-GS1, are also presented in this paper.

scholarly journals Weakly-Supervised Simultaneous Evidence Identification and Segmentation for Automated Glaucoma Diagnosis

2019 ◽  
Vol 33 ◽  
pp. 809-816 ◽  
Author(s):  
Rongchang Zhao ◽  
Wangmin Liao ◽  
Beiji Zou ◽  
Zailiang Chen ◽  
Shuo Li
Keyword(s):  
Optic Disc ◽  
Constrained Clustering ◽  
Fundus Images ◽  
Evidence Map ◽  
Multi Scale ◽  
Glaucoma Diagnosis ◽  
Diagnostic Labels ◽  
Clinically Significant ◽  
Weakly Supervised ◽  
Fully Connected

Evidence identification, optic disc segmentation and automated glaucoma diagnosis are the most clinically significant tasks for clinicians to assess fundus images. However, delivering the three tasks simultaneously is extremely challenging due to the high variability of fundus structure and lack of datasets with complete annotations. In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis. The WSMTL method only uses weak-label data with binary diagnostic labels (normal/glaucoma) for training, while obtains pixel-level segmentation mask and diagnosis for testing. The WSMTL is constituted by a skip and densely connected CNN to capture multi-scale discriminative representation of fundus structure; a well-designed pyramid integration structure to generate high-resolution evidence map for evidence identification, in which the pixels with higher value represent higher confidence to highlight the abnormalities; a constrained clustering branch for optic disc segmentation; and a fully-connected discriminator for automated glaucoma diagnosis. Experimental results show that our proposed WSMTL effectively and simultaneously delivers evidence identification, optic disc segmentation (89.6% TP Dice), and accurate glaucoma diagnosis (92.4% AUC). This endows our WSMTL a great potential for the effective clinical assessment of glaucoma.

scholarly journals AUTOMATED GLAUCOMA DETECTION USING HYBRID FEATURE EXTRACTION IN RETINAL FUNDUS IMAGES

2013 ◽  
Vol 13 (01) ◽  
pp. 1350011 ◽  
Author(s):  
M MUTHU RAMA KRISHNAN ◽  
OLIVER FAUST
Keyword(s):  
Feature Extraction ◽  
Mass Screening ◽  
Risk Index ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Glaucoma Detection ◽  
Hybrid Feature Extraction

Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.

scholarly journals Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhou An ◽  
Kazuko Omodaka ◽  
Kazuki Hashimoto ◽  
Satoru Tsuda ◽  
Yukihiro Shiga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Optic Disc ◽  
Learning System ◽  
Ganglion Cell Complex ◽  
Fundus Images ◽  
Optical Coherence ◽  
Glaucoma Diagnosis ◽  
Rnfl Thickness ◽  
Color Fundus Images

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

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