Glaucoma Detection Using Features of Optic Nerve Head, CDR and ISNT from Fundus Image of Eye

2021 ◽  
pp. 316-327
Author(s):  
Kartik Thakkar ◽  
Kinjan Chauhan ◽  
Anand Sudhalkar ◽  
Aditya Sudhalkar ◽  
Ravi Gulati
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Fundus Image ◽  
Glaucoma Detection

Quantitative depth analysis of optic nerve head using stereo retinal fundus image pair

2008 ◽  
Vol 13 (6) ◽  
pp. 064026 ◽  
Author(s):  
Toshiaki Nakagawa ◽  
Takayoshi Suzuki ◽  
Yoshinori Hayashi ◽  
Yutaka Mizukusa ◽  
Yuji Hatanaka ◽  
...  
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Image Pair ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Depth Analysis ◽  
Retinal Fundus

Segmentation of Optic Nerve Head for Glaucoma Detection using Fundus images

2014 ◽  
Vol 7 (2) ◽  
pp. 697-705 ◽  
Author(s):  
Ganesh Babu.T.R ◽  
R. Sathishkumar ◽  
Rengaraj Venkatesh
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Fundus Images ◽  
Glaucoma Detection

scholarly journals A novel glaucomatous representation method based on Radon and wavelet transform

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Beiji Zou ◽  
Changlong Chen ◽  
Rongchang Zhao ◽  
Pingbo Ouyang ◽  
Chengzhang Zhu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Optic Nerve ◽  
Large Scale ◽  
Structural Changes ◽  
Feature Representation ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fundus Image ◽  
Visual Patterns ◽  
Glaucoma Detection

Abstract Background Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. Method We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. Results The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. Conclusion In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.

scholarly journals Optic Disc Segmentation by Balloon Snake with Texture from Color Fundus Image

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jinyang Sun ◽  
Fangjun Luan ◽  
Hanhui Wu
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Optic Nerve Head ◽  
Template Matching ◽  
Image Force ◽  
Superior Performance ◽  
Image Texture ◽  
Cluster Method ◽  
Initial Contour ◽  
Fundus Image

A well-established method for diagnosis of glaucoma is the examination of the optic nerve head based on fundus image as glaucomatous patients tend to have larger cup-to-disc ratios. The difficulty of optic segmentation is due to the fuzzy boundaries and peripapillary atrophy (PPA). In this paper a novel method for optic nerve head segmentation is proposed. It uses template matching to find the region of interest (ROI). The method of vessel erasing in the ROI is based on PDE inpainting which will make the boundary smoother. A novel optic disc segmentation approach using image texture is explored in this paper. A cluster method based on image texture is employed before the optic disc segmentation step to remove the edge noise such as cup boundary and vessels. We replace image force in the snake with image texture and the initial contour of the balloon snake is inside the optic disc to avoid the PPA. The experimental results show the superior performance of the proposed method when compared to some traditional segmentation approaches. An average segmentation dice coefficient of 94% has been obtained.

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