scholarly journals An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 695 ◽  
Author(s):  
Emil Saeed ◽  
Maciej Szymkowski ◽  
Khalid Saeed ◽  
Zofia Mariak
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Ophthalmological Examination ◽  
Hard Exudate ◽  
Telemedicine System ◽  
Hard Exudates ◽  
Accuracy Level ◽  
Fundus Photographs ◽  
Classical Image ◽  
Solution Accuracy

Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis—50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described.

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 Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Abhilash Goud Marupally ◽  
Kiran Kumar Vupparaboina ◽  
Hari Kumar Peguda ◽  
Ashutosh Richhariya ◽  
Soumya Jana ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Hard Exudates ◽  
Automated Quantification ◽  
Fundus Photographs

Image Processing for the Detection of Diabetic Retinopathy and Diabetic Macular Edema

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Manjula Sri ◽  
K.M. M. Rao
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Blood Vessels ◽  
Diabetic Macular Edema ◽  
Severity Index ◽  
Diabetic Patients ◽  
Color Analysis ◽  
Hard Exudates ◽  
Patients With Diabetes

Diabetic retinopathy (DR) and diabetic macular edema (DME) are common microvascular retinal diseases in patients with diabetes. The diabetic patients may have a sudden and devastating impact on visual acuity, in the long run leading to blindness. Advanced stages of DR are characterized by the growth of abnormal retinal blood vessels secondary to ischemia. These blood vessels grow in an attempt to supply oxygenated blood to the hypoxic retina. At any time during the progression of DR, patients with diabetes can also develop DME, which involves retinal thickening in the macular area. In the present paper, algorithms are developed to detect DR and DME. For detecting DR the abnormalities in the retina blood vessels are detected by classifying the common abnormalities namely microaneurisms, hard exudates, heammorages and cotton wool spots. DME is detected by finding the nearness of Hard exudate to macula. The macula and hard exudates are localized using image processing techniques. Severity of DME is assessed based on the nearest exudates, their area and color analysis. The algorithm is tested with 65 DR and DME images with severity index 0, 1 and 2 from MESSIDOR database.

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.

scholarly journals Intravitreal aflibercept partially reverses severe non-proliferative diabetic retinopathy in treatment-naïve patients

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098536
Author(s):  
Yuan Tao ◽  
Pengfei Jiang ◽  
Min Liu ◽  
Ying Liu ◽  
Lihua Song ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Intravitreal Injection ◽  
Proliferative Diabetic Retinopathy ◽  
Posterior Pole ◽  
Foveal Thickness ◽  
Hard Exudates ◽  
Central Foveal Thickness ◽  
Treatment Naïve ◽  
The Mean ◽  
Retinal Microaneurysms

Objective To evaluate whether diabetic retinopathy can be reversed after aflibercept, based on improvements in diabetic macular edema, hard exudates (HEs) of the posterior pole, and retinal microaneurysms (MAs). Methods This was a single-center retrospective study of 30 patients (34 eyes) with severe non-proliferative diabetic retinopathy (NPDR) who were treated between August and October 2018. Best-corrected visual acuity (BCVA), central foveal thickness (CFT), area of HEs, and number of MAs were compared before and after treatment. Results The mean patient age was 61.4 ± 7.1 years; 14 patients (46.7%) were men. The mean number of injections per patient was 3.5 ± 0.5. The time between the last injection and the last follow-up was 82 days (range, 78–110 days). Six months after the first intravitreal injection, significant improvement was observed in BCVA (from 0.70 ± 0.18 to 0.42 ± 0.19 logMAR), CFT (from 377.17 ± 60.41 to 261.21 ± 31.50 µm), and number of MAs (from 182.2 ± 77.4 to 101.5 ± 59.6). Observations over 6 months after the first intravitreal injection showed a statistically significant reduction in the area of HEs (P = 0.007). No adverse events occurred during the treatment period. Conclusion Diabetic retinopathy might be partially reversed by aflibercept treatment, as indicated by BCVA, CFT, number of MAs, and area of HEs.

Quantum generative adversarial networks for learning and loading quantum image in noisy environment

2021 ◽  
pp. 2150360
Author(s):  
Wanghao Ren ◽  
Zhiming Li ◽  
Yiming Huang ◽  
Runqiu Guo ◽  
Lansheng Feng ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantum Circuit ◽  
Quantum Image Processing ◽  
Quantum Circuits ◽  
Generative Adversarial Networks ◽  
Channel Noise ◽  
Quantum Image ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Classical Image

Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

Sign in / Sign up

Export Citation Format

Share Document