scholarly journals Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6549
Author(s):  
Roberto Romero-Oraá ◽  
María García ◽  
Javier Oraá-Pérez ◽  
María I. López-Gálvez ◽  
Roberto Hornero
Keyword(s):  
Diabetic Retinopathy ◽  
Lesion Detection ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Public Database ◽  
Diagnostic Systems ◽  
Hard Exudates ◽  
The Public ◽  
Proprietary Database

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

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 A Fuzzy clustering Approach for Micro Aneurysm Lesion Detection in Diabetic Retinopathy

2020 ◽  
Vol 11 (SPL4) ◽  
pp. 503-511
Author(s):  
Rajesh S R ◽  
Kanniga E ◽  
Sundararajan M
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Lesion Detection ◽  
Human Vision ◽  
Computer Assisted ◽  
Effective Care ◽  
Key Factor ◽  
Assisted Diagnosis ◽  
Occurrence Matrix ◽  
Effective Diagnosis

Diabetic retinopathy (DR) be the significant difficulty of diabetes, and micro aneurysm (MA) is an earliest diabetic retinopathy lesion, making early detection of MA a key factor in diabetic retinopathy. DR is a direct or indirect effect on human vision caused by chronic diabetes. During its early stages DR is asymptomatic, and the late diagnosis leads to undeviating vision loss. The computer-assisted diagnosis helps with prompt and effective care, with the aid of medical photos. MA mark the beginning of DR making it a vital screening stage for this disorder. Diabetic retinopathy is a persistent infection of eye that can be the reason of blindness unless it is diagnosed and treated in due course. Early discovery with analysis of diabetic retinopathy is vital to vision preservation of patient. Precise recognition of MA be the crucial method towards early diagnosis of DR, since they occur as the first symptom of the disease. The segmentation of MA is performed using the Fuzzy C algorithm, and the extraction of features is performed with Gray Level Co-occurrence Matrix ( GLCM) as the set of characteristic for KNN. This technique aims to improve classification accuracy within an ensemble. A procedure is suggested here that recognizes the first DR sign called MA using images from the retinal fundus. Effective diagnosis of DR is very critical in the defense of patients' right to see. The procedure proposed is tested using publicly available databases of retinal images and greater accuracy is achieved.

scholarly journals Automated Detection and Classification of Diabetic Retinopathy using ConvNets

2021 ◽  
Vol 9 (10) ◽  
pp. 1360-1369
Author(s):  
Aavani B
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Confusion Matrix ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Data Set ◽  
Permanent Loss ◽  
Process Computer ◽  
Degree Classification

Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT

scholarly journals A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

2014 ◽  
Vol 14 (3) ◽  
pp. 5494-5499
Author(s):  
Sreeparna Banerjee ◽  
Diptoneel Kayal
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Visual Inspection ◽  
Fundus Image ◽  
Analysis Program ◽  
Hard Exudates ◽  
Lighting Conditions ◽  
Retinal Fundus Image ◽  
Inner Diameter ◽  
Retinal Fundus

Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

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 Detection of Hard Exudates Based on Morphological Feature Extraction

2018 ◽  
Vol 11 (1) ◽  
pp. 215-225 ◽  
Author(s):  
Shilpa Joshi ◽  
P. T. Karule
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Morphological Feature ◽  
Vision Loss ◽  
Automated System ◽  
Diabetic Patients ◽  
Computationally Efficient ◽  
Image Processing Algorithm ◽  
Hard Exudates ◽  
Processing Techniques

In diabetic patients, the chances of vision loss are higher. These issues related to vision can be diagnosed using diabetic retinopathy. It is one of the very important diseases amongst all retinal pathologies. One of the simplest changes observed on the eye due to diabetes is lesions in yellow or white color i.e. hard exudates (EX). It appears bright in fundus images and hence it is the most important to detect using image processing algorithm. In this work the proposed algorithm used is based on morphological feature extraction. Post processing techniques are required to separate out EX from other bright artefacts such as cotton wool spot and optic disc. The performance evaluation of the proposed algorithm shows the sensitivity of 96.7%, specificity 85.4% and accuracy of 91% on image level detection on Diaretdb1 database and achieved higher accuracy on publicly available e-ophtha EX retinal image database in terms of lesion level detection. It is computationally efficient as an automated system to assist the ophthalmologist. Early detection of hard exudates is crucial for diagnosing the stages of diabetic retinopathy to prevent blindness.

scholarly journals Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

2019 ◽  
Vol 25 (2) ◽  
pp. 131-139
Author(s):  
Sathya D Janaki ◽  
K Geetha
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Retinal Image ◽  
Visual Analysis ◽  
Support Vector ◽  
Retinal Images ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Soft Computing Techniques ◽  
Cae System

Abstract Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.

scholarly journals Prediction of diabetic retinopathy using machine learning techniques

2021 ◽  
Author(s):  
T Jemima Jebaseeli ◽  
◽  
C. Anand Deva Durai ◽  
Salem Alelyani ◽  
Mohammed Saleh Alsaqer ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Early Stage ◽  
Image Data ◽  
Histogram Equalization ◽  
Machine Learning Techniques ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Resonance Theory ◽  
Adaptive Resonance

Diabetic Retinopathy (DR) is the complicatedness of diabetes that happens due to macular degeneration among Type II diabetic patients. The early symptom of this disease is predicted through annual eye checkups. Hence, one can save their vision at an early stage. Later on, it prompts retinal detachment. There is a requirement for awareness among diabetic patients about this disease to prevent their life from vision misfortune. Along these lines, there is a need for a computer-assisted method to analyze the disease. The proposed system used Adaptive Histogram Equalization (AHE) technique for image enhancement, Hop Field Neural Network for blood vessel segmentation, and Adaptive Resonance Theory (ART) for blood vessel classification. The proposed system analyzes the disease and classifies the disease level effectively with high accuracy. Also, the system notifies the users about the stages of the disease. The proposed system is evaluated with the clinical as well as open fundus image data sets like DRIVE, STARE, MESSIDOR, HRF, DRIONS, and REVIEW for diabetic retinopathy prediction. Also, physicians evaluated the system and concluded that the proposed system does not deviate from the quality of disease analysis and grading. The proposed techniques accomplished 99.99% accuracy. The system is evaluated by the ophthalmologists and witnesses that the proposed system has not veered off as far as quality.

scholarly journals Validation of computer-aided diagnosis of diabetic retinopathy from retinal photographs of diabetic patients from Telecamps

2021 ◽  
Author(s):  
Dr SHEILA JOHN ◽  
Dr Sangeetha Srinivasan ◽  
Dr Prof Natarajan Sundaram
Keyword(s):  
Diabetic Retinopathy ◽  
Tamil Nadu ◽  
Characteristic Curve ◽  
Posterior Pole ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Automated Algorithm ◽  
Screening Performance ◽  
Patients With Diabetes

Objective: To validate an algorithm previously developed by the Healthcare Technology Innovation Centre, IIT Madras, India for screening of diabetic retinopathy (DR),  in fundus images of diabetic patients from telecamps to examine the screening performance for DR. Design: Photographs of patients with diabetes were examined using the automated algorithm for the detection of DR   Setting: Mobile Teleophthalmology camps were conducted in two districts in Tamil Nadu, India from Jan 2015 to May 2017. Participants: 939 eyes of 472 diabetic patients were examined at Mobile Teleophthalmology camps in Thiruvallur and Kanchipuram districts, Tamil Nadu, India,. Fundus images were obtained (40-45-degree posterior pole in each eye) for all patients using a nonmydriatic fundus camera by the fundus photographer. Main Outcome Measures: Fundus images were evaluated for presence or absence of DR using a computer-assisted algorithm, by an ophthalmologist at a tertiary eye care centre (reference standard) and by a fundus photographer. Results: The algorithm demonstrated 85% sensitivity and 80% specificity in detecting DR compared to ophthalmologist. The area under the receiver operating characteristic curve was 0.69 (95%CI=0.65 to 0.73). The algorithm identified 100% of vision-threatening retinopathy just like the ophthalmologist. When compared to the photographer, the algorithm demonstrated 81% sensitivity and 78% specificity. The sensitivity of the photographer to detect DR was found to be 86% and specificity was 99% in detecting DR compared to ophthalmologist. Conclusions: The algorithm can detect the presence or absence of DR in diabetic patients. All findings of vision-threatening retinopathy could be detected with reasonable accuracy and will help to reduce the workload for human graders in remote areas.

scholarly journals Lesion Detection and Classification techniques for Diabetic Retinopathy

2020 ◽  
Vol 9 (3) ◽  
pp. 3436-3441
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Lesion Detection ◽  
Classification Techniques ◽  
The Public ◽  
Detection Systems ◽  
Regular Screening ◽  
Public Datasets

Diabetes is a worldwide spread disease which is increasing rapidly and found in all age people. Diabetic Retinopathy is a retinal abnormality caused by diabetes. Which can lead to permanent vision loss or blindness. As Diabetic Retinopathy pathology damages retina without any early symptoms, it is very important to do the regular screening of retina and detection of Retinopathy. Ophthalmologist does the identification of Retinopathy manually which is time consuming and error prone. Hence, there is a need for early and correct automatic detection of Diabetic Retinopathy. Many researches have done for detection using Image Processing, Artificial Intelligence, Neural Network and Machine Learning. This paper presents a review on Diabetic Retinopathy Detection systems. This review highlights the public datasets available for the evaluation of the detection systems with different segmentation and classification techniques. We have discussed the analysis of different classification and segmentation techniques used in DR detection.

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