scholarly journals Blood Vessels Detection of Diabetic Retinopathy from Retinal Fundus Image using Image Processing Techniques

2020 ◽  
Vol 18 (44) ◽  
pp. 1-16
Author(s):  
Faleh H. Mahmood
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Vision Loss ◽  
Digital Camera ◽  
Eye Diseases ◽  
Morphological Operations ◽  
Structuring Element ◽  
Processing Techniques

 abstract Early detection of eye diseases can forestall visual deficiency and vision loss. There are several types of human eye diseases, for example, diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. Diabetic retinopathy (DR) which is brought about by diabetes causes the retinal vessels harmed and blood leakage in the retina. Retinal blood vessels have a huge job in the detection and treatment of different retinal diseases. Thus, retinal vasculature extraction is significant to help experts for the finding and treatment of systematic diseases. Accordingly, early detection and consequent treatment are fundamental for influenced patients to protect their vision. The aim of this paper is to detect blood vessels from the digital fundus images. In this research, a novel methodology was introduced to separate retinal blood vessel network. The suggested system in this research involves four stages, after image acquisition, the pre-processes of the image to preparing and improving the image quality is the first stage. Morphological operations are used for the detection of blood vessels. In this research, we will use two morphological operations: erosion and dilation. These two operations have two inputs, a binary image, and a structuring element object. We will use two morphological processes (boundary extraction and top, bottom hat transform). Before these operations, we will use applying a canny edge detector technique to obtain the edges of the retina image. The technique is tried on shading retinal pictures acquired from STARE and DRIVE databases which are accessible on the web as well as the samples of retinal images were obtained from the digital camera from Ibn Al-Haytham specialist Hospital for Eye in Baghdad, Iraq. Good results and effective were obtained for blood vessel detected and extract  

scholarly journals Retinal vessel feature extraction from fundus image using image processing techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 687
Author(s):  
R. Lavanya ◽  
G. K. Rajini ◽  
G. Vidhya Sagar
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Vessel Detection ◽  
Proper Diagnosis ◽  
Processing Techniques

Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology. 

scholarly journals Diabetic Exudate Detection in Color Retinal Images

2019 ◽  
Vol 19 ◽  
pp. 7510-7518
Author(s):  
Dalia Ali
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
High Performance ◽  
Vision Loss ◽  
Vascular Complication ◽  
Processing Method ◽  
Retinal Images ◽  
Morphological Operations ◽  
Positive Predicted Value

Diabetic retinopathy is a vascular complication of long-term diabetes. It causes damage to the small blood vessels positioned in the retina. These damaged blood vessels affect the macula and lead to vision loss. Exudates are one of the early signs of diabetic retinopathy disease in the retinal image, which occurs due to built-up of lipidic accumulation within the retina. In this paper, an image processing method is presented for diabetic exudates detection. First, high performance pre-processing is applied not only for de-noising and normalization but also to remove artefacts and reflection that could mislead exudates detection. Then, morphological operations are applied for the final candidate segmentation. Eight region features are extracted from the exudate region then random forest classifier is applied to differentiate between exudates and non-exudates region. The proposed method is evaluated using e_ophtha_EX dataset, achieving 80% sensitivity and 77% positive predicted value.

scholarly journals Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
D. Siva Sundhara Raja ◽  
S. Vasuki
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Anisotropic Diffusion ◽  
Vision Loss ◽  
Automatic Detection ◽  
Diabetic Patients ◽  
Morphological Operations ◽  
Anisotropic Diffusion Filter ◽  
Retinal Blood Vessels ◽  
Detection And Diagnosis

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

scholarly journals Identification of Hemorrhages in Iris Using Hybrid Morphological Method

2020 ◽  
Vol 8 (3) ◽  
pp. 01-05
Author(s):  
Robbi Rahim
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Diabetic Patients ◽  
Morphological Operations ◽  
Critical Factors ◽  
Image Subtraction ◽  
The Past ◽  
Image Thinning

In the field of ophthalmology, hemorrhage is the term used more often because of increasing diabetic patients. It’s a challenge amidst the ophthalmologist to distinguish the hemorrhage from the blood vessels, these lands in various problems. In the past various techniques were employed for the detection of the hemorrhage but they were not so accurate and often encountered misclassification between hemorrhage and blood vessels. Precise detection and classification of hemorrhage and blood vessel is very important in the diagnosis of many problems. This paper depicts a mechanized procedure for recognizing hemorrhages in fundus pictures. The acknowledgment of hemorrhages is one of the critical factors in the early finish of diabetic retinopathy. The algorithm proceeds through several steps such as image enhancement, image subtraction, morphological operations such as image thresholding, image strengthening, image thinning, erosion, morphological closing, image complement to suppress blood vessels and to highlight the hemorrhages

scholarly journals Severity Level Detection in Retinal Fundus Images for Diabetic Retinopathy

2020 ◽  
Vol 9 (4) ◽  
pp. 1298-1302
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Gabor Filter ◽  
Morphological Operations ◽  
Fundus Images ◽  
Fundus Image ◽  
Severity Level ◽  
Chemical Solutions ◽  
Processing Techniques ◽  
Retinal Fundus

Diabetic Retinopathy is the disease caused for diabetic people which doesn’t have symptoms in the first phase. As it progresses, it becomes symptomatic. This disease, sometimes, might lead to complete blindness. Red lesions contain microaneurysms, haemorrhages and exudates. This work focuses on detection of red lesions in fundus image. Ophthalmologists use pupil dilation of chemical solutions in order to detect the abnormality which takes time and also causes irritation to patients. Image processing techniques are used to avoid these limitations. Morphological operations are used to identify the pixels belonging to the red lesions. Gabor filter is used for separating the blood vessels. Some spots are formed near macular region because blood vessels become leaky which leads to exudates. The severity level of the disease is determined by finding the distance between lesions and macular region. The disease is considered as severe if the distance between them is closer and confirms as less if the distance is far.

scholarly journals Fundus Image Analysis to Detect Abnormalities in Diabetic Retinopathy using Computer Aided Design Tools - A Review

2020 ◽  
Vol 9 (6) ◽  
pp. 224-232
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Computer Aided Design ◽  
Vision Loss ◽  
Early Stage ◽  
Human Beings ◽  
Fundus Image ◽  
Detection Techniques

Diabetic retinopathy is becoming a major threat to visual loss in human beings. Many researchers are working to develop early detection techniques, which may reduce the risk of vision loss using image-processing techniques like image enhancement and segmentation. Improving the quality of medical images to detect the disease at an early stage is crucial for further medication. It is gaining more focus with automated techniques for machine learning. Filtering and morphological operators enhance image contrast and interested region can be extracted using segmentation techniques from the fundus image of the retina. For feature analysis the optical disk, localization of blood vessels and segmentation are very useful to observe the parameters like area, length and perimeter of blood vessels etc. Algorithms for this analysis include preprocessing, segmentation, feature extraction and classification. This paper tries to give a detailed review of various image-processing methods used in early detection of diabetic retinopathy and future insights to develop algorithms, which reduces clinician’s time for diagnosis and pathogenesis.

scholarly journals An Improved Segmentation Technique for Early Detection of Exudates of Diabetic Retinopathy Disease

2019 ◽  
pp. 2520-2530
Author(s):  
Faleh H. Mahmood ◽  
Shahad Abdul-Jabbar Aziz
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Final Stage ◽  
Minimum Distance ◽  
Vision Loss ◽  
Clear Indication ◽  
Segmentation Method ◽  
Essential Step ◽  
Three Stages

Diabetic retinopathy (DR) is a diabetes- caused disease that is associated with  leakage of fluid from the blood vessels into the retina, leading to its damage. It is one of the most common diseases that can lead to weak vision and even blindness. Exudates is a clear indication of diabetic retinopathy, which is the main cause of blindness in people with diabetes. Therefore, early detection of exudates is a crucial and essential step to prevent blindness and vision loss is in the analysis of digital diabetic retinopathy systems. This paper presents an improved approach for detection of exudates in retina image using supervised-unsupervised Minimum Distance (MD) segmentation method. The suggested system includes three stages; First, after image acquisition, it is pre-processed for preparing and improving its quality. Second, the simple toward interpretation and analysis of image is segmentation as another stage.      In this research, we presented a method which is used for segmentation of     exudates by the adaptive (supervised-unsupervised) Minimum Distance (MD)  creation segmentation algorithm with two non-overlapping clusters. The method was proposed based on its performance compared with other methods and followed by exudates extraction as a final stage. This proposed framework helps the ophthalmologists to distinguish the problem earlier, which enables them to recommend a superior medication for forestalling further retinal harm.

scholarly journals Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 274 ◽  
Author(s):  
Thippa Reddy Gadekallu ◽  
Neelu Khare ◽  
Sweta Bhattacharya ◽  
Saurabh Singh ◽  
Praveen Kumar Reddy Maddikunta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Dimensionality Reduction ◽  
Vision Loss ◽  
Principal Component ◽  
Screening Methods ◽  
Learning Approaches ◽  
Time Period

Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

scholarly journals Andalusian program for early detection of diabetic retinopathy: implementation and 15-year follow-up of a population-based screening program in Andalusia, Southern Spain

2020 ◽  
Vol 8 (1) ◽  
pp. e001622 ◽  
Author(s):  
Rafael Rodriguez-Acuña ◽  
Eduardo Mayoral ◽  
Manuel Aguilar-Diosdado ◽  
Reyes Rave ◽  
Beatriz Oyarzabal ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Vision Loss ◽  
Screening Program ◽  
Target Population ◽  
Population Based ◽  
Annual Incidence ◽  
Population Coverage ◽  
Visual Disability ◽  
The Impact

IntroductionDiabetic retinopathy (DR) is a preventable cause of vision loss and blindness worldwide. We aim at analyzing the impact of a population-based screening program of DR using retinal photography with remote reading in terms of population coverage, diagnosis of asymptomatic DR and impact on visual disability, in the region of Andalusia, Spain, in the period 2005–2019.Research design and methodsDescriptive study. Sociodemographic and clinical features included in the Andalusian program for early detection of diabetic retinopathy (APDR) were analyzed. Population coverage, annual incidence of DR, and DR severity gradation were analyzed. Estimated data on prevalence and incidence of legal blindness due to DR were included.Results407 762 patients with at least one successful DR examination during the study period were included. Most of the performed retinographies (784 584, 84.3%) were ‘non-pathological.’ Asymptomatic DR was detected in 52 748 (5.9%) retinographies, most of them (94.2%) being classified as ‘mild to moderate non-proliferative DR.’ DR was detected in 44 815 patients, while sight-threatening DR (STDR) in 6256 patients; cumulative incidence of DR was 11.0% and STDR was 1.5%, as DR and STDR was detected in 44 815 and 6256 patients, respectively. Annual incidence risk per patient recruitment year progressively decreased from 22.0% by January 2005 to 3.2% by June 2019.ConclusionsImplementation of a long-term population-based screening program for early detection of DR is technically feasible and clinically viable. Thus, after 15 years of existence, the program has enabled the screening of the vast majority of the target population allowing the optimization of healthcare resources and the identification of asymptomatic DR.

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