Lesion detection in ultra-wide field retinal images for diabetic retinopathy diagnosis

2018 ◽  
Author(s):  
Anastasia Levenkova ◽  
Michael Kalloniatis ◽  
Angelica Ly ◽  
Arthur Ho ◽  
Arcot Sowmya
Keyword(s):  
Diabetic Retinopathy ◽  
Lesion Detection ◽  
Retinal Images ◽  
Wide Field

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

2017 ◽  
Author(s):  
Anastasia Levenkova ◽  
Arcot Sowmya ◽  
Michael Kalloniatis ◽  
Angelica Ly ◽  
Arthur Ho
Keyword(s):  
Diabetic Retinopathy ◽  
Automatic Detection ◽  
Retinal Images ◽  
Wide Field

scholarly journals An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yinan Zhang ◽  
Mingqiang An
Keyword(s):  
Diabetic Retinopathy ◽  
Active Learning ◽  
Dimensional Space ◽  
Lesion Detection ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Retinal Images ◽  
Screening System ◽  
Learning Classifier ◽  
Screening Cost

Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, andKNearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.

scholarly journals Diabetic Retinopathy Detection Using Walter-Klein Contrast Enhancement

2018 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Diabetic Retinopathy ◽  
Developed Countries ◽  
Lesion Detection ◽  
Eye Disease ◽  
Retinal Images ◽  
Diabetic Patients ◽  
Multiple Classifiers ◽  
Retinal Lesion ◽  
The Developed Countries ◽  
Screening Systems

When sugar level (glucose) in the blood fails to regulate the insulin properly in human body, diabetic is occurred. The effect of diabetic on eye causes diabetic retinopathy. Diabetic retinopathy (DR) is a serious eye disease that originates from diabetes mellitus and is the most common cause of blindness in the developed countries. Therefore, much effort has been made to establish reliable computer aided screening systems based on color fund us images. Diabetic Retinopathy is one of a complicated diabetes which can cause blindness. It is a metabolic disordered patients perceive no symptoms until the disease is at late stage. So early detection and proper treatment has to be ensured. To serve this purpose, various automated systems have been designed. We propose an ensemble-based framework for retinal lesion detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of of Retinal Lesion detectors, namely preprocessing methods and candidate extractors. The presence of micro aneurysms in the eye is one of the early signs of diabetic retinopathy. We analzye the input retinal images of the Diabetic patients and we can classify that the patient is affected by DR or not. If not affected, they are normal patient. If they are affected, further it classifies the different stages of diabetic retinopathy affected patients such as Mild, Moderate and Severe.

scholarly journals Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images

2020 ◽  
pp. bjophthalmol-2019-315394
Author(s):  
Abraham Olvera-Barrios ◽  
Tjebo FC Heeren ◽  
Konstantinos Balaskas ◽  
Ryan Chambers ◽  
Louis Bolter ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Screening Programme ◽  
Proliferative Retinopathy ◽  
Retinal Images ◽  
Wide Field ◽  
Diabetic Retinopathy Screening ◽  
Eye Screening ◽  
Confocal Scanning ◽  
Diabetic Eye Screening ◽  
Retinopathy Screening

BackgroundPhotographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.MethodsCross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images.ResultsWe included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images.ConclusionEyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of ‘no retinopathy’ and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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