Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space

2022 ◽  
Author(s):  
Buket Toptaş ◽  
Murat Toptaş ◽  
Davut Hanbay
Keyword(s):  
Optic Disc ◽  
Color Space ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Optic Disc Localization ◽  
Retinal Fundus

scholarly journals Early Detection Of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Level Set Segmentation

2019 ◽  
Vol 8 (5C) ◽  
pp. 1342-1349
Keyword(s):  
Early Detection ◽  
Optic Disc ◽  
Region Of Interest ◽  
Clinical Parameter ◽  
Fundus Image ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Standard Value ◽  
Retinal Fundus

Glaucoma is a malady of the optic nerve brought about by the expansion in the intraocular weight of the eye. It for the most part influences the optic plate by expanding the cup size. In this proposed method the clinical parameter such as vertical optic cup to disk ratio (CDR) is determined to identify the glaucomatous disease. The segmentation of optic disc (OD) and optic cup in retinal fundus image is an important step in the determination of CDR. Optic Disc is extracted from the fundus image by circular region of interest with Hough transformation. Linear regression fit is used to find the Gold standard value for the experimentally obtained CDR A Bayesian classifier is used to train the classifier set of CDR values obtained. Results produced from the classification obtain a accuracy of 94.28%, sensitivity of 94.38% and specificity of 94.11%. ROC curve is plotted to study the relation between specificity and sensitivity of the CDR and GSV. This proposed approach is robust in segmentation and the region boundaries are precise and is able to yield regions more homogeneous which can be used for objective mass screening of retinal images for early detection of Glaucoma.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Non-Invasive Contrast Normalisation and Denosing Technique for the Retinal Fundus Image

2016 ◽  
Vol 3 (3) ◽  
pp. 265-279 ◽  
Author(s):  
Toufique Ahmed Soomro ◽  
Junbin Gao
Keyword(s):  
Fundus Image ◽  
Non Invasive ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals A Simple Method for Optic Disk Segmentation from Retinal Fundus Image

2014 ◽  
Vol 6 (11) ◽  
pp. 36-42 ◽  
Author(s):  
Adithya Kusuma Whardana ◽  
◽  
Nanik Suciati ◽  
Keyword(s):  
Optic Disk ◽  
Fundus Image ◽  
Simple Method ◽  
Retinal Fundus Image ◽  
Retinal Fundus

Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus
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