scholarly journals Median Filtering for Optic Disc Segmentation in Retinal Image

2018 ◽  
pp. 73-80 ◽  
Author(s):  
Ibnu Fiandono ◽  
Kartika Firdausy
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Ground Truth ◽  
Retinal Images ◽  
Median Filtering ◽  
Routine Examination ◽  
Software Application ◽  
Ground Truth Image ◽  
Eye Retina ◽  
Vision Disturbance

One of diabetic complications is vision disturbance known as Diabetic Retinopathy (DR). Early detection becomes vital to prevent the development of DR by routine examination of the patient's eyes. This study developed a software application that can automatically segment the image of the eye retina, expected to assist eye specialists in performing DR detection using computer aids. The system implementation uses Visual C# 2010 software with 36 retinal images of the Drishti-GS1 dataset. The clinical features of the retina that can be used to detect DR are microaneurysms, hard and soft exudates, hemorrhages, neovascularization, and macular edema. Optic Disc (OD) segmentation is an important step to detect these features. This study aimed to detect OD using red channel, inversion, contrast enhancement, median filtering, and thresholding. The final result of the OD segmentation was validated by measuring the positive predictive value (PPV). The result of PPV for the detection of OD to ground truth image reached 90.924%. 

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

Automatic Localization of the Optic Disc Center in Retinal Images based on Angle Detection in Curvature Scale Space

2015 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
A. Elbalaoui ◽  
Mohamed Fakir ◽  
M. Boutaounte ◽  
A. Merbouha
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Retinal Image ◽  
Scale Space ◽  
Second Step ◽  
Retinal Images ◽  
Anatomical Structures ◽  
Disc Center ◽  
Curvature Scale Space ◽  
Automatic Localization

Digital images of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The localization of the Optic Disc (OD) center is the first and necessary step identification and segmentation of anatomical structures and in pathological retinal images. From the center of the optic disc spreads the major blood vessels of the retina. Therefore, by considering the high number of vessels and the high number of the angles resulted from the vessels crossing, the authors propose a new method based on the number of angles in the vicinity of optic disc for localization of the center of optic disc. The first step is pre-processing of retinal image for separate the fundus from its background and increase the contrast between contours. In the second step, the authors use the Curvature Scale Space (CSS) for angle detection. In the next step, they move a window about the size of optic disc to count the number of corners. In the final step, they use the center of windows which has the most number of corners for localizing the optic disc center. The proposed method is evaluated on DRIVE, CHASE_DB1 and STARE databases and the success rate is 100, 100 and 96.3%, respectively.

scholarly journals Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Kemal Akyol ◽  
Baha Şen ◽  
Şafak Bayır
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Texture Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Disease Diagnosis ◽  
Retinal Images ◽  
Hard Exudate ◽  
Visual Dictionary ◽  
Public Datasets

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

scholarly journals Automatic Detection of Optic Disc for Diabetic Retinopathy

2020 ◽  
Vol 9 (7) ◽  
pp. 1013-1015
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Early Stage ◽  
Vital Role ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Canny Edge ◽  
Total Blindness ◽  
Processing Techniques

Diabetic Retinopathy affects the retina of the eye and eventually it may lead to total visual impairment. Total blindness can be avoided by detecting Diabetic Retinopathy at an early stage. Various manual tests are used by the doctors to detect the presence of disease, but they are tedious and expensive. Some of the features of Diabetic Retinopathy are exudates, haemorrhages and micro aneurysms. Detection and removal of optic disc plays a vital role in extraction of these features. This paper focuses on detection of optic disc using various image processing techniques, algorithms such as Canny edge, Circular Hough (CHT). Retinal images from IDRiD, Diaret_db0, Diaret_db1, Chasedb and Messidor datasets were used.

scholarly journals Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm

Ophthalmology ◽  
2018 ◽  
pp. 53-68 ◽  
Author(s):  
Upendra Kumar
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Detection System ◽  
Retinal Images ◽  
Filter Output ◽  
Automatic Detection System ◽  
Filter Approach ◽  
Eye Retina

Considering Retinal image as textured image, its texture based segmentation is required to identify the presence of retinal diseases. This pre-processing is important in automatic detection system for recognizing the abnormality present in the retinal images. Likewise, the proposed system mainly focused on diabetic retinopathy disease caused into eye –retina, generally leads to eye-blindness. Inspired from robust human's texture based segmentation capability, a mathematical model of the eye was formulated. A texture based Gabor filter was applied to get the output feature helping in detecting the abnormality and deriving statistical properties, further used in segmentation and classification. This work deals with the better separation of various clusters of Gabor filter output features, in order to get better segmentation efficiency. This was also followed by formalizing an objective function to tune filter parameters with Gradient descent and further Genetic Algorithm. This paper showed both qualitative and quantitative segmentation results with improved efficiency.

scholarly journals Optic Disc and Optic Cup Segmentation for Glaucoma Detection from Blur Retinal Images Using Improved Mask-RCNN

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tahira Nazir ◽  
Aun Irtaza ◽  
Valery Starovoitov
Keyword(s):  
Optic Disc ◽  
Data Augmentation ◽  
Ground Truth ◽  
Retinal Images ◽  
Screening Process ◽  
Glaucoma Detection ◽  
Key Points ◽  
Optic Cup ◽  
Efficiency And Effectiveness ◽  
Slow Progress

Glaucoma is a fatal eye disease that harms the optic disc (OD) and optic cup (OC) and results into blindness in progressed phases. Because of slow progress, the disease exhibits a small number of symptoms in the initial stages, therefore causing the disease identification to be a complicated task. So, a fully automatic framework is mandatory, which can support the screening process and increase the chances of disease detection in the early stages. In this paper, we deal with the localization and segmentation of the OD and OC for glaucoma detection from blur retinal images. We have presented a novel method that is Densenet-77-based Mask-RCNN to overcome the challenges of the glaucoma detection. Initially, we have performed the data augmentation step together with adding blurriness in samples to increase the diversity of data. Then, we have generated the annotations from ground-truth (GT) images. After that, the Densenet-77 framework is employed at the feature extraction level of Mask-RCNN to compute the deep key points. Finally, the calculated features are used to localize and segment the OD and OC by the custom Mask-RCNN model. For performance evaluation, we have used the ORIGA dataset that is publicly available. Furthermore, we have performed cross-dataset validation on the HRF database to show the robustness of the presented framework. The presented framework has achieved an average precision, recall, F-measure, and IOU as 0.965, 0.963, 0.97, and 0.972, respectively. The proposed method achieved remarkable performance in terms of both efficiency and effectiveness as compared to the latest techniques under the presence of blurring, noise, and light variations.

scholarly journals Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

2021 ◽  
Vol 5 (3) ◽  
pp. 242
Author(s):  
Dafwen Toresa ◽  
Mohamad Azrul Edzwan Shahril ◽  
Nor Hazlyna Harun ◽  
Juhaida Abu Bakar ◽  
Hidra Amnur
Keyword(s):  
Diabetic Retinopathy ◽  
Imaging System ◽  
System Development ◽  
Ground Truth ◽  
Automated Detection ◽  
Retinal Images ◽  
Segmentation Method ◽  
Hard Exudates ◽  
Detection And Counting

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.

Automatic Localization of the Optic Disc Center in Retinal Images Based on Angle Detection in Curvature Scale Space

2017 ◽  
pp. 679-692
Author(s):  
A. Elbalaoui ◽  
Mohamed Fakir ◽  
M. Boutaounte ◽  
A. Merbouha
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Digital Images ◽  
Scale Space ◽  
New Method ◽  
Retinal Images ◽  
Anatomical Structures ◽  
Disc Center ◽  
Curvature Scale Space ◽  
Automatic Localization

Digital images of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The localization of the Optic Disc (OD) center is the first and necessary step identification and segmentation of anatomical structures and in pathological retinal images. From the center of the optic disc spreads the major blood vessels of the retina. Therefore, by considering the high number of vessels and the high number of the angles resulted from the vessels crossing, the authors propose a new method based on the number of angles in the vicinity of optic disc for localization of the center of optic disc. The first step is pre-processing of retinal image for separate the fundus from its background and increase the contrast between contours. In the second step, the authors use the Curvature Scale Space (CSS) for angle detection. In the next step, they move a window about the size of optic disc to count the number of corners. In the final step, they use the center of windows which has the most number of corners for localizing the optic disc center. The proposed method is evaluated on DRIVE, CHASE_DB1 and STARE databases and the success rate is 100, 100 and 96.3%, respectively.

Image Analysis for Exudate Detection in Retinal Images

2010 ◽  
pp. 198-203 ◽  
Author(s):  
Gerald Schaefer ◽  
Albert Clos
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Early Diagnosis ◽  
Ground Truth ◽  
Experimental Results ◽  
Retinal Images ◽  
Fundus Image ◽  
Common Causes ◽  
Image Set

Diabetic retinopathy is recognised as one of the most common causes of blindness. Early diagnosis is important and is based on detection of features such as exudates during eye fundus image screening. In this chapter it is shown how areas corresponding to exudates can be automatically detected using a neural network that, following contrast enhancement and vessel and optic disc extraction steps, classifies each image pixel as exudate or non-exudate. Experimental results on an image set with known ground truth verify the usefulness of the presented approach.

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