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 Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images

2020 ◽  
Vol 10 (11) ◽  
pp. 3833 ◽  
Author(s):  
Haidar Almubarak ◽  
Yakoub Bazi ◽  
Naif Alajlan
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Optic Nerve Head ◽  
Fine Tuning ◽  
Fundus Images ◽  
Two Stage ◽  
Second Stage ◽  
Retinal Fundus Images ◽  
Tuning Strategy ◽  
Retinal Fundus

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.

Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images

2020 ◽  
Vol 43 (4) ◽  
pp. 1265-1277
Author(s):  
Mohamed Bouacheria ◽  
Yazid Cherfa ◽  
Assia Cherfa ◽  
Noureddine Belkhamsa
Keyword(s):  
Optic Nerve ◽  
Random Forest ◽  
Optic Nerve Head ◽  
Random Forest Classifier ◽  
Fundus Images ◽  
Glaucoma Screening

scholarly journals Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis

2010 ◽  
Vol 23 (4) ◽  
pp. 438-453 ◽  
Author(s):  
Rangaraj M. Rangayyan ◽  
Xiaolu Zhu ◽  
Fábio J. Ayres ◽  
Anna L. Ells
Keyword(s):  
Optic Nerve ◽  
Phase Portrait ◽  
Optic Nerve Head ◽  
Gabor Filters ◽  
Fundus Images ◽  
Phase Portrait Analysis

scholarly journals Detection of the Optic Nerve Head in Fundus Images of the Retina Using the Hough Transform for Circles

2009 ◽  
Vol 23 (3) ◽  
pp. 332-341 ◽  
Author(s):  
Xiaolu Zhu ◽  
Rangaraj M. Rangayyan ◽  
Anna L. Ells
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Hough Transform ◽  
Fundus Images

Effect of two different preprocessing steps in detection of optic nerve head in fundus images

2017 ◽  
Author(s):  
Meysam Tavakoli ◽  
Mahdieh Nazar ◽  
Alireza Mehdizadeh
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Fundus Images

3D reconstruction of the optic nerve head using stereo fundus images for computer-aided diagnosis of glaucoma

2010 ◽  
Author(s):  
Li Tang ◽  
Young H. Kwon ◽  
Wallace L. M. Alward ◽  
Emily C. Greenlee ◽  
Kyungmoo Lee ◽  
...  
Keyword(s):  
Optic Nerve ◽  
3D Reconstruction ◽  
Optic Nerve Head ◽  
Computer Aided Diagnosis ◽  
Fundus Images ◽  
Computer Aided ◽  
Aided Diagnosis
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