Color Histogram- and Smartphone-Based Diabetic Retinopathy Detection System

2018 ◽  
pp. 669-678 ◽  
Author(s):  
Nikita Kashyap ◽  
Dharmendra Kumar Singh ◽  
Girish Kumar Singh
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Color Histogram

35-LB: Reliability of an Artificial Intelligence (AI) Diabetic Retinopathy (DR) Detection System

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 35-LB
Author(s):  
ELI IPP ◽  
DAVID R. LILJENQUIST
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Detection System

CURVELET FUSION ENHACEMENT BASED EVALUATION OF DIABETIC RETINOPATHY BY THE IDENTIFICATION OF EXUDATES IN OPTIC COLOR FUNDUS IMAGES

2016 ◽  
Vol 28 (06) ◽  
pp. 1650046
Author(s):  
V. Ratna Bhargavi ◽  
Ranjan K. Senapati
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Detection System ◽  
Clinical Signs ◽  
Posterior Part ◽  
Curvelet Transform ◽  
Optic Disk ◽  
Support Vector ◽  
Fundus Images ◽  
Statistical Measures

Rapid growth of Diabetes mellitus in people causes damage to posterior part of eye vessel structures. Diabetic retinopathy (DR) is an important hurdle in diabetic people and it causes lesion formation in retina due to retinal vessel structures damage. Bright lesions (BLs) or exudates are initial clinical signs of DR. Early BLs detection can help avoiding vision loss. The severity can be recognized based on number of BLs formed in the color fundus image. Manually diagnosing a large amount of images is time consuming. So a computerized DR grading and BLs detection system is proposed. In this paper for BLs detection, curvelet fusion enhancement is done initially because bright objects maps to largest coefficients in an image by utilizing the curvelet transform, so that BLs can be recognized in the retina easily. Then optic disk (OD) appearance is similar to BLs and vessel structures are barriers for lesion exact detection and moreover OD falsely classified as BLs and that increases false positives in classification. So these structures are segmented and eliminated by thresholding techniques. Various features were obtained from detected BLs. Publicly available databases are used for DR severity testing. 260 fundus images were used for the performance evaluation of proposed work. The support vector machine classifier (SVM) used to separate fundus images in various levels of DR based on feature set extracted. The proposed system that obtained the statistical measures were sensitivity 100%, specificity 95.4% and accuracy 97.74%. Compared to existing state-of-art techniques, the proposed work obtained better results in terms of sensitivity, specificity and accuracy.

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 Evaluation of Performance of Decision Tree, Support Vector Machine and Probabilistic Neural Network Classifiers in a Mobile Based Diabetes Retinopathy Detection System

2019 ◽  
pp. 1-9
Author(s):  
O. D. Fenwa ◽  
O. O. Alo ◽  
I. O. Omotoso
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Decision Tree ◽  
Medical Condition ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Rejection Rate ◽  
Support Vector ◽  
Diabetes Retinopathy

Diabetic Retinopathy (DR) is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. Aim: The focus of this paper is to evaluate the performance of Decision Tree (DT), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) Classifiers in Diabetes Retinopathy Detection. Results: Corresponding results showed SVM has the best classification strength by achieving Recognition Accuracy (RA) of 98.50%, while PNN and DT achieved RA of 97.60% and 89.20% respectively. In terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), SVM has the least values of 7.21, 8.10 while DT and PNN showed 11.10, 9.30 and 13.21, 10.10 respectively. However, in this paper a Mobile based Diabetes Retinopathy Detection System was developed to make the system available for the masses for early detection of the disease.

Automated Diagnostic Hybrid Lesion Detection System for Diabetic Retinopathy Abnormalities

2020 ◽  
Vol 10 (4) ◽  
pp. 494-507
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Statistical Investigation ◽  
Lesion Detection ◽  
Diagnostic Systems ◽  
Detection Techniques ◽  
Good Classification ◽  
Detailed Statistical Analysis ◽  
Vessel Extraction ◽  
Timely Treatment

Background: Early diagnosis, monitoring disease progression, and timely treatment of Diabetic Retinopathy (DR) abnormalities can efficiently prevent visual loss. A prediction system for the early intervention and prevention of eye diseases is important. The contrast of raw fundus image is also a hindrance in effective manual lesion detection technique. Methods: In this research paper, an automated lesion detection diagnostic scheme has been proposed for early detection of retinal abnormalities of red and yellow pathological lesions. The algorithm of the proposed Hybrid Lesion Detection (HLD) includes retinal image pre-processing, blood vessel extraction, optical disc localization and detection stages for detecting the presence of diabetic retinopathy lesions. Automated diagnostic systems assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. Detailed statistical analysis is performed on the extracted shape, intensity and GLCM features and the optimal features are selected to classify DR abnormalities. Exhaustive statistical investigation of the proposed approach using visual and empirical analysis resulted in 31 significant features. Results: The results show that the HLD approach achieved good classification results in terms of three statistical indices: accuracy, 98.9%; sensitivity, 97.8%; and specificity, 100% with significantly less complexity. Conclusion: The proposed technique with optimal features demonstrates improvement in accuracy as compared to state of the art techniques using the same database.

scholarly journals An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
George Michael Saleh ◽  
James Wawrzynski ◽  
Silvestro Caputo ◽  
Tunde Peto ◽  
Lutfiah Ismail Al Turk ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Detection Algorithm ◽  
Rapid Identification ◽  
Automated Detection ◽  
Automated System ◽  
Racial Groups ◽  
Fundus Images ◽  
Screening Service ◽  
Wide Range

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system’s performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

BOUNDARY SEGMENTATION AND DETECTION OF DIABETIC RETINOPATHY (DR) IN FUNDUS IMAGE

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
R. Samad ◽  
M. S. F. Nasarudin ◽  
M. Mustafa ◽  
D. Pebrianti ◽  
N. R. H. Abdullah
Keyword(s):  
Diabetic Retinopathy ◽  
Medical Image ◽  
Processing Time ◽  
Detection System ◽  
Computer Aided Detection ◽  
Fundus Image ◽  
Medical Field ◽  
Computer Aided ◽  
Automatic Detection System ◽  
Number Of Iterations

Recently, the automatic detection system or Computer-Aided Detection (CAD) is widely developed in the medical field to screen or diagnose the medical image. This paper presents the boundary segmentation and detection of Diabetic Retinopathy (DR) in fundus image. The proposed method uses Fuzzy C-Means for clustering and detect the boundary of the DR object. The number of cluster used in this work is 3 and the average number of iterations is 28.The DR region is successfully detected by FCM and the average processing time is 1.235s.  

Mathematical morphology for microaneurysm detection in fundus images

2019 ◽  
Vol 30 (5) ◽  
pp. 1135-1142 ◽  
Author(s):  
Shilpa Joshi ◽  
PT Karule
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Automated Detection ◽  
Morphological Processing ◽  
Candidate Selection ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Time Saving ◽  
Increased Risk ◽  
Sight Loss

Aim: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. Methods: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. Results: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. Conclusion: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

Sign in / Sign up

Export Citation Format

Share Document