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.

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

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

The region of interest localization for glaucoma analysis from retinal fundus image using deep learning

2018 ◽  
Vol 165 ◽  
pp. 25-35 ◽  
Author(s):  
Anirban Mitra ◽  
Priya Shankar Banerjee ◽  
Sudipta Roy ◽  
Somasis Roy ◽  
Sanjit Kumar Setua
Keyword(s):  
Deep Learning ◽  
Region Of Interest ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

2020 ◽  
Vol 4 (02) ◽  
pp. 105-109
Author(s):  
Pulung Hendro Prastyo ◽  
Amin Siddiq Sumi ◽  
Annis Nuraini
Keyword(s):  
Ground Truth ◽  
Batch Size ◽  
Fundus Images ◽  
Fundus Image ◽  
Dice Coefficient ◽  
Input Size ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

scholarly journals Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

2020 ◽  
Vol 10 (11) ◽  
pp. 3777 ◽  
Author(s):  
Yun Jiang ◽  
Falin Wang ◽  
Jing Gao ◽  
Simin Cao
Keyword(s):  
Blood Vessel ◽  
Optic Disc ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood vessel segmentation, can effectively identify these diseases. The existing methods lack sufficient discrimination power for the fundus image and are easily affected by pathological regions. This paper proposes a novel multi-path recurrent U-Net architecture to achieve the segmentation of retinal fundus images. The effectiveness of the proposed network structure was proved by two segmentation tasks: optic disc and optic cup segmentation and retinal vessel segmentation. Our method achieved state-of-the-art results in the segmentation of the Drishti-GS1 dataset. Regarding optic disc segmentation, the accuracy and Dice values reached 0.9967 and 0.9817, respectively; as regards optic cup segmentation, the accuracy and Dice values reached 0.9950 and 0.8921, respectively. Our proposed method was also verified on the retinal blood vessel segmentation dataset DRIVE and achieved a good accuracy rate.

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