scholarly journals An Effective Screening System to Detect Diabetic Retinopathy by using Dehazing Technique

2020 ◽  
Vol 8 (5) ◽  
pp. 3746-3749
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Mean Square ◽  
Screening System ◽  
Fundus Image ◽  
Effective Screening ◽  
Disease Condition ◽  
Clinical Data Analysis ◽  
Prompt Diagnosis

The Diabetic Retinopathy (DR) is playing a crucial role in clinical data analysis to diagnose abnormality in retina. Many situations the early stage of patient is not aware of any symptoms until it is too late for effective treatment. The abnormality in the blood vessels of diabetics, a way will be paved for prompt diagnosis of DR. In this work, we proposed the Dehazing method of fundus image to detect and classify the disease condition based on changes in blood vessels using thresholding segmentation technique using mean square error (MSE). Then formulate the area of extracted blood vessels in the subsequent analysis to classify accurately.

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.

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.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

scholarly journals A Decision Support Framework for Automated Screening of Diabetic Retinopathy

2006 ◽  
Vol 2006 ◽  
pp. 1-8 ◽  
Author(s):  
P. Kahai ◽  
K. R. Namuduri ◽  
H. Thompson
Keyword(s):  
Diabetic Retinopathy ◽  
Decision Support ◽  
Early Stage ◽  
Optimality Criteria ◽  
Real World Data ◽  
Early Signs ◽  
Decision Support Framework ◽  
Detection Rule ◽  
Automated Screening ◽  
Prompt Diagnosis

The early signs of diabetic retinopathy (DR) are depicted by microaneurysms among other signs. A prompt diagnosis when the disease is at the early stage can help prevent irreversible damages to the diabetic eye. In this paper, we propose a decision support system (DSS) for automated screening of early signs of diabetic retinopathy. Classification schemes for deducing the presence or absence of DR are developed and tested. The detection rule is based on binary-hypothesis testing problem which simplifies the problem to yes/no decisions. An analysis of the performance of the Bayes optimality criteria applied to DR is also presented. The proposed DSS is evaluated on the real-world data. The results suggest that by biasing the classifier towards DR detection, it is possible to make the classifier achieve good sensitivity.

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%.

scholarly journals Classification of Diabetic Retinopathy Features using Bag of Feature Model

2019 ◽  
Vol 9 (1) ◽  
pp. 381-385
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Medical Image Analysis ◽  
Feature Learning ◽  
Initial Step ◽  
Feature Model ◽  
Automated System ◽  
Morphological Operations ◽  
Speeded Up Robust Features

Deep learning (DL) as well as feature learning by unsupervised methods have made tremendous consideration in the past decades because of its great and dynamic capacity to change input data into high level depictions by means of various machine learning (ML) methods and approaches. Therefore these interests have also showed a fast and steady growth in the arena of medical image analysis, especially in Diabetic Retinopathy (DR) classification. On contradiction, manual interpretation involves excessive processing time, large amount of expertise and work. Sternness of the DR is analyzed relative to the existence of Microaneurysms (MAs), Exudates (EXs) and Hemorrhages(HEs). Spotting of DR in its early stage is crucial and important to avoid blindness. This paper proposes an algorithm to build an automated system to extract the above mentioned DR features which are the elemental and initial signs of diabetic retinopathy. Initial step in this algorithm is preprocessing of the original image. The next step in this features extraction algorithms is elimination of optic disc (OD) and blood vessels which have similar characteristic with these features. Blood vessels are segmented using Multi-Level Adaptive Thresholding. OD is segmented using morphological operations. Feature extraction and classification is achieved by using deep Bag of Feature (BoF) model which uses Speeded Up Robust Features Our method achieved 100% acuuracy in DRIVE database and over 90% accuracy for e-OPTHA database. Thus, the proposed methodology represents a track towards precise and highly automated DR diagnosis on a large substantial scale along with better sensitivity and specificity.

scholarly journals An efficient method for early stage detection of diabetic retinopathy

2017 ◽  
Vol 7 (1.1) ◽  
pp. 414
Author(s):  
S D. Shirbahadurkar ◽  
V M. Mane ◽  
D V. Jadhav
Keyword(s):  
Diabetic Retinopathy ◽  
Decision Tree ◽  
Early Stage ◽  
Human Vision ◽  
Phase System ◽  
Correct Identification ◽  
Fundus Image ◽  
Decision Tree Classifier ◽  
Accurate Identification ◽  
Tree Classifier

Diabetic Retinopathy (DR) is one of the leading causes of blindness. The early detection and treatment of DR is significant to save the human vision. The presence of microaneurysms (MAs) is the first sign of the disease. The correct identification of MAs is an essential for finding of DR at the early stages. In this paper, we propose a three phase system for efficient recognition of MAs. The tentative MA lesions are recovered from the fundus image in the first stage. To accurately classify an extracted candidate region into MA or non-MA, the second stage prepares an attribute vector for each tentative MA lesion based on shape, intensity and statistical properties. The third stage is a classification step to classify as MAs and Non-MAs for early stage detection of DR. We present a holoentropy enabled decision tree classifier which combines entropy and total correlation. The best feature for decision tree is selected based on holoentropy to enhance the correctness of the classification. The implemented system is experimented using fundus image database DIARETDB1. The proposed method achieved an overall accuracy of 97.67%.The proposed system has detected the MAs with higher performance using simple features and holoentropy based decision tree classifier. The proposed system is suitable for early stage detection of DR through the accurate identification of MAs.

scholarly journals Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Juan Elisha Widyaya ◽  
Setia Budi
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Careful Examination ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

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.

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