An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method. SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.

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

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237216500460 ◽

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.

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An efficient automated system for detection of diabetic retinopathy from fundus images using support vector machine and bayesian classifiers

2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) ◽

10.1109/icceet.2012.6203804 ◽

2012 ◽

Cited By ~ 15

Author(s):

K. Narasimhan ◽

V.C. Neha ◽

K. Vijayarekha

Keyword(s):

Support Vector Machine ◽

Diabetic Retinopathy ◽

Automated System ◽

Support Vector ◽

Fundus Images ◽

Bayesian Classifiers

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Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

International Journal of Computer Network and Information Security ◽

10.5815/ijcnis.2021.01.05 ◽

2021 ◽

Vol 13 (1) ◽

pp. 47-60

Author(s):

Raveendra K ◽

◽

Ravi J

Keyword(s):

Feature Extraction ◽

Performance Evaluation ◽

Face Recognition ◽

Frequency Domain ◽

Recognition Rate ◽

Curvelet Transform ◽

Recognition System ◽

Support Vector ◽

Svm Classifier ◽

Fast Discrete Curvelet Transform

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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A Machine Vision based Detection & Classification of Neovascularisation in Retinal Blood Vessels

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1017.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1468-1471

Keyword(s):

Diabetic Retinopathy ◽

Proliferative Diabetic Retinopathy ◽

Clustering Algorithm ◽

Automated System ◽

Support Vector ◽

Svm Classifier ◽

Retinal Blood Vessels ◽

Fuzzy C Means Clustering ◽

Sift Features

Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. In this proposed system techniques are introduced to detect and classify neovascularisation. Input fundus image is preprocessed by median filtering and further new vessels are segmented by using Fuzzy c-means clustering algorithm. After segmentation SIFT features are extracted and are used to train support vector machine (SVM) classifier. This automated system has been tested for 70 fundus images and accuracy of 96% is achieved

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760.v1 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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Greedy segmentation based diabetic retinopathy identification using curvelet transform and scale invariant features

Journal of Engineering Research ◽

10.36909/jer.v9i1.9407 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Pelin Görgel ◽

Keyword(s):

Diabetic Retinopathy ◽

Vision Loss ◽

Curvelet Transform ◽

Optic Disk ◽

Support Vector ◽

Morphological Operations ◽

Fundus Images ◽

Scale Invariant ◽

Cad System ◽

Active Population

Diabetic retinopathy (DR) is the major reason of vision loss in the active population. It can usually be prevented by regulating the blood glucose and providing a timely treatment. DR has clinical features recognized by the experts including the blood vessel area, exudates, neovascularization, hemorrhages, and microaneurysm. Because DR has some varieties and complexities due to its geometrical and haemodynamic features, it is hard and time-consuming to detect DR in manual diagnosis. In Computer Aided Diagnosis (CAD) systems, the features of DR fundus images are detected using computer vision techniques. In this paper, a CAD system is proposed, which distinguishes automatically whether the fundus is normal or it suffers from diabetic retinopathy disease. As preprocess morphological operations like filtering, opening, and dilation are applied to the images firstly, then, Optic Disk (OD) segmentation is implemented using Greedy algorithm. Because of the intensity of an OD is similar to some DR intensities, OD regions are removed from the fundus images for an accurate feature extraction. The features extracted with Curvelet Transform (CT) and Scale Invariant Feature Transform (SIFT), respectively, are concatenated to provide a feature set that defines the fundus data optimally. Finally, the feature set is given to the Support Vector Machines (SVM), K-Nearest Neighborhood (KNN), and Naïve–Bayes (NB) classifiers for the DR identification purpose. The proposed method achieves the highest accuracy and sensitivity as 92.8% and 97.6%, respectively, with SVM and specificity as 92.5% with KNN classifier.

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Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

Mobile Information Systems ◽

10.1155/2019/6142839 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 10

Author(s):

Yung-Hui Li ◽

Nai-Ning Yeh ◽

Shih-Jen Chen ◽

Yu-Chien Chung

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Deep Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Computer Assisted ◽

Fundus Images ◽

Max Pooling

Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.

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Diabetic Retinopathy Grading by Digital Curvelet Transform

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/761901 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 22

Author(s):

Shirin Hajeb Mohammad Alipour ◽

Hossein Rabbani ◽

Mohammad Reza Akhlaghi

Keyword(s):

Diabetic Retinopathy ◽

Region Of Interest ◽

Curvelet Transform ◽

Optic Disk ◽

Foveal Avascular Zone ◽

Support Vector ◽

Fundus Images ◽

Fundus Fluorescein Angiography ◽

Avascular Zone ◽

Color Fundus Images

One of the major complications of diabetes is diabetic retinopathy. As manual analysis and diagnosis of large amount of images are time consuming, automatic detection and grading of diabetic retinopathy are desired. In this paper, we use fundus fluorescein angiography and color fundus images simultaneously, extract 6 features employing curvelet transform, and feed them to support vector machine in order to determine diabetic retinopathy severity stages. These features are area of blood vessels, area, regularity of foveal avascular zone, and the number of micro-aneurisms therein, total number of micro-aneurisms, and area of exudates. In order to extract exudates and vessels, we respectively modify curvelet coefficients of color fundus images and angiograms. The end points of extracted vessels in predefined region of interest based on optic disk are connected together to segment foveal avascular zone region. To extract micro-aneurisms from angiogram, first extracted vessels are subtracted from original image, and after removing detected background by morphological operators and enhancing bright small pixels, micro-aneurisms are detected. 70 patients were involved in this study to classify diabetic retinopathy into 3 groups, that is, (1) no diabetic retinopathy, (2) mild/moderate nonproliferative diabetic retinopathy, (3) severe nonproliferative/proliferative diabetic retinopathy, and our simulations show that the proposed system has sensitivity and specificity of 100% for grading.

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