scholarly journals Glaucoma Detection from Color Fundus Images

2012 ◽  
pp. 261-264
Author(s):  
Madhusudan Mishra ◽  
Malaya Kumar Nath ◽  
Samarendra Dandapat
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Vision Loss ◽  
Pathological Condition ◽  
Pathological Process ◽  
Contour Method ◽  
Fundus Images ◽  
Progressive Neurodegeneration ◽  
Color Fundus Images ◽  
Cup To Disc Ratio

Glaucoma is a pathological condition, progressive neurodegeneration of the optic nerve, which causes vision loss. The damage to the optic nerve occurs due to the increase in pressure within the eye. Glaucoma is evaluated by monitoring intra ocular pressure (IOP), visual field and the optic disc appearance (cup-to-disc ratio). Cup-to disc ratio (CDR) is normally a time invariant feature. Therefore, it is one of the most accepted indicator of this disease and the disease progression. In this paper, active contour method is used to find the CDR from the color fundus images to determine pathological process of glaucoma. The method is applied on 25 nos of color fundus images obtained from optic disc organization UK having normal and pathological images. The proposed technique able to categorize all the glaucoma disease images.

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.

Local comparison of cup to disc ratio in right and left eyes based on fusion of color fundus images and OCT B-scans

2019 ◽  
Vol 51 ◽  
pp. 30-41 ◽  
Author(s):  
Marzieh Mokhtari ◽  
Hossein Rabbani ◽  
Alireza Mehri-Dehnavi ◽  
Raheleh Kafieh ◽  
Mohammad-Reza Akhlaghi ◽  
...  
Keyword(s):  
Fundus Images ◽  
Local Comparison ◽  
Color Fundus Images ◽  
Cup To Disc Ratio

scholarly journals Automatic Diagnosis of Glaucoma using Image Processing Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 3984-3987
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Vision Loss ◽  
Early Stage ◽  
Threshold Value ◽  
Processing Technique ◽  
Least Square ◽  
Disc Height ◽  
Image Processing Technique ◽  
Human Eye

Glaucoma is a human eye condition which will affect the optic nerve present in the retina. This condition occurs due to the abnormal ocular pressure in human eye. If it is not diagnosed and treated well in advance, it may lead to blindness. This is the major problem of elderly people all over the world. The best way to avoid vision loss due to glaucoma is to detect the disease at the early stage and treat it as soon as possible. These are the keys to prevent blindness. As vision is an important organ in human body it is advisable to keep it healthy. The optic cup in the retina will be pulled in towards the optic nerve away from the optic disc. At one point, the cup will be detached from the retina, causing blindness. So if one can monitor by measuring the optic disc to cup ratio, the progression of glaucoma can be diagnosed earlier. The proposed method detects the optic disc and cup using thresholding method. Direct least square fitting algorithm is used here to fit the ellipse in order to calculate the cup height and disc height. Then the ratio is calculated. If the calculated ratio is above the threshold value, it is considered as glaucoma affected eye otherwise not. The CDR is calculated using the formula VDH/VCH (Vertical Disc Height to the Vertical Cup Height). Thus, the proposed method helps to automatically detect the glaucoma disease with better sensitivity and specificity.

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 Developments for Diabetic Retinopathy Detection and Classification: A Review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 134
Author(s):  
Pooja M. Pawar ◽  
Avinash J. Agrawal
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Eye Disease ◽  
Retinal Images ◽  
Screening Methods ◽  
Fundus Images ◽  
Insulin Deficiency ◽  
Vascular Disorder ◽  
Impaired Metabolism ◽  
Color Fundus Images

Diabetes is characterized by impaired metabolism of glucose caused by insulin deficiency. Diabetic retinopathy is the eye disease, is caused by retinal damage which is generally formed as a result of diabetes mellitus. It is a serious vascular disorder for which early detection and the treatment are required to inhibit the intense vision loss. Also, the diagnosis entails skilled professionals for detection because non-automatic screening methods are very time consuming and are not that efficient for a large number of retinal images. This paper provides a broad review of various techniques and methodologies used by the authors for diabetic retinopathy detection and classification. Furthermore, most recent work and developments are studied in this paper. We are proposing an advanced deep learning CNN approach for automatic diagnosis of DR from color fundus images.  

Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images

2016 ◽  
Vol 36 (6) ◽  
pp. 795-809 ◽  
Author(s):  
Maitreya Maity ◽  
Dev Kumar Das ◽  
Dhiraj Manohar Dhane ◽  
Chandan Chakraborty ◽  
Anirudhha Maiti
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Fundus Images ◽  
Contour Model ◽  
Color Fundus Images

scholarly journals Swollen Optic Disc and Sinusitis

2017 ◽  
Vol 8 (2) ◽  
pp. 421-424 ◽  
Author(s):  
Chiara Del Noce ◽  
Filippo Marchi ◽  
Giacomo Sollini ◽  
Michele Iester
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Pathological Condition ◽  
Computed Tomography Imaging ◽  
Radiological Investigation ◽  
Sphenoidal Sinus ◽  
Ocular Symptoms ◽  
Optic Disc Swelling ◽  
Set Up ◽  
The Relationship

Purpose: To present a case of optic disc swelling caused by sinusitis. Methods: Ocular symptoms were investigated using computed tomography imaging of the facial bones to detect the relationship between the sinus inflammation and the optic nerve. Results: A particular configuration of the optic nerve was detected. Optic nerve course through the inflamed sphenoidal sinus is a condition associated with a greater risk of inflammation. Conclusion: Sinusitis is a rare but treatable cause of optic neuritis. The choice of the correct radiological investigation to be done to set up a proper treatment of the sinus pathological condition is also essential for the resolution of ocular symptoms.

scholarly journals Detection of Glaucoma in Retinal Fundus Images using Fast Fuzzy C Mean Clustering

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Fluid Pressure ◽  
Fundus Images ◽  
New Approach ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Glaucoma is one of the major causes of vision loss in today’s world. Glaucoma is a disease in the eye where fluid pressure in the eye increases; if it is not timely cured, the patient may lose their vision. Glaucoma can be detected by examining boundary of optics cup and optics disc acquired from fundus images. The proposed method suggest automatic detect the boundary of optics cup and optics disc with processing of fundus images. This paper explores the new approach fast fuzzy C-mean technique for segmenting the optic disc and optic cup in fundus images. Results evaluated by fast fuzzy C mean a technique is faster than fuzzy C-mean method. The proposed method reported results to 91.91%, 90.49% and 90.17% when tested on DRIONS, DRIVE and STARE on publicly available databases of fundus images.

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