Color Retinal Images Research Articles

Automatic Parallel Detection of Neovascularization from Retinal Images Using Ensemble of Extreme Learning Machine.

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role on the identification and classification of diabetes retinopathy. In this paper, an automatic parallel detection framework for neovascularization with color retinal images using ensemble of extreme learning machine is proposed. The framework employs two Map-Reduce Jobs to extract features and trains Extreme Learning Machine models. Ensemble methods such as bagging, subspace partitioning and cross validating are used to increase the accuracy. The framework is evaluated with retinal images from MESSIDOR database. Experimental results show the framework can improve the detection accuracy, as well as speedup the processing time to 22 times on average.

A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier.

Diabetic Retinopathy is the major cause of blindness for diabetics in which the retina is damaged. Regular screening system help in detecting the early symptoms like exudates, which are due to the leakage of blood pressure of vessels. The significant role of proposed system is detecting the hard exudates in prevention of visual loss and blindness. Many researchers studied and investigated about detecting the exudates region but not satisfied with their results. Fundamental medical image processing steps with different techniques are implemented by the proposed system. Random Forest is a novel classification which is applied on color retinal images able to classify cluster of data with high accuracy. The performance of the proposed system is obtained by analyzing the accuracy obtained from the Random Forest classifier. These images are obtained from Diabetic Retinopathy Database (DIARETDB) database. The simulation results are obtained with the help of MATLAB 2018. By applying novel classification techniques improves the automatic detection of hard exudates from color retinal images. The achieved accuracy is compared with existing classifiers since the proposed Random Forest classifier provides the accuracy of 99.89% applied on color retinal images.

Drusen diagnosis comparison between hyper-spectral and color retinal images.

Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.

Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation.

Diabetic retinopathy (DR) is one of the most common causes of visual impairment. Automatic detection of hard exudates (HE) from retinal photographs is an important step for detection of DR. However, most of existing algorithms for HE detection are complex and inefficient. We have developed and evaluated an automatic retinal image processing algorithm for HE detection using dynamic threshold and fuzzy C-means clustering (FCM) followed by support vector machine (SVM) for classification. The proposed algorithm consisted of four main stages: (i) imaging preprocessing; (ii) localization of optic disc (OD); (iii) determination of candidate HE using dynamic threshold in combination with global threshold based on FCM; and (iv) extraction of eight texture features from the candidate HE region, which were then fed into an SVM classifier for automatic HE classification. The proposed algorithm was trained and cross-validated (10 fold) on a publicly available e-ophtha EX database (47 images) on pixel-level, achieving the overall average sensitivity, PPV, and F-score of 76.5%, 82.7%, and 76.7%. It was tested on another independent DIARETDB1 database (89 images) with the overall average sensitivity, specificity, and accuracy of 97.5%, 97.8%, and 97.7%, respectively. In summary, the satisfactory evaluation results on both retinal imaging databases demonstrated the effectiveness of our proposed algorithm for automatic HE detection, by using dynamic threshold and FCM followed by an SVM for classification.

Agreement study between color and IR retinal images based on retinal vasculature morphological parameters

BackgroundColor fundus photography have been extensively used to explore the link between retinal morphology changes associated with various disease i.e. Diabetic Retinopathy, Glaucoma. The development of multimodal imaging system that integrates Infrared Scanning Laser Ophthalmoscope (IR-SLO) and Optical Coherence Tomography (OCT) could help in studying these diseases at an early stage. The aim of this study was to test the agreement between the retinal vasculature parameters from the Infrared images obtained from optical coherence tomography and color fundus imaging.MethodsThe IR and Color retinal images were obtained from 16 volunteer participants and seven retinal vessel parameters, i.e. Fractal Dimension (FD), Average Angle (ABA), Total Angle Count (TAC), Tortuosity (ST), Vessel/Background ratio (VBR), Central Retinal Arteriolar Equivalent (CRAE) and Central Retinal Venular Equivalent (CRVE) were extracted from these retinal images using Retinal Image Vasculature Assessment software (RIVAS) and Integrative Vessel Analysis (IVAN).ResultsThe Bland Altman plot was used to investigate the agreement between the two modalities. The paired sample t-test was used to assess the presence of fixed bias and the slope of Least Square Regression (LSR) line for the presence of proportional bias.The paired sample t-test showed that there was no statistically significant difference between Color and IR based on retinal vessel features (all p values > 0.05). LSR also revealed no statistically significant difference in the retinal vessel features between Color and IR.ConclusionThis study has revealed that there is a fair agreement between Color and IR images based on retinal vessel features. This research has shown that it is possible to use IR images of the retina to measure the retinal vasculature parameters which has the advantage of being flash-less, can be used even if there is opacity due to cataract, and can be performed along with OCT on the same device.

Color retinal image enhancement using luminosity and quantile based contrast enhancement

Retinal imaging is used to diagnose common eye diseases. But retinal images that suffer from image blurring, uneven illumination and low contrast become useless for further diagnosis by automated systems. In this work, we have proposed a new method for overall contrast enhancement of the color retinal images. Initially, a gain matrix of luminance values which is obtained by adaptive gamma correction method is used to enhance all three color channels of the images. After that quantile-based histogram equalization is used to enhance overall visibility of the images. Enhancement results of the proposed method are compared with several other existing methods. Performance of the proposed method is evaluated on all images of publicly available Messidor database. Based on the assessment measure we have shown that the proposed method is able to enhance the contrast of given color retinal image without changing its structural information. The proposed technique is appeared to accomplish superior image enhancement with sufficient contrast enhancement, these enhancement results are better than other related techniques. This technique for color retinal image enhancement might be utilized to help ophthalmologists in the more productive screening of retinal ailments, what’s more, being developed of enhanced robotized image examination for clinical finding.

Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.

Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.

Efficient approach for the automatic detection of haemorrhages in colour retinal images

Advances in the eye care telemedicine system aid the diabetic patients in remote areas to stop the unwanted visit to ophthalmologist, reduces overall cost, time and money. Diabetic retinopathy, which is the primary cause of sight loss, has the most common symptoms like microaneurysms, haemorrhages, cotton-wool spots, exudates and drusen. In this work, an efficient approach for the automatic detection of haemorrhages in colour retinal images is proposed and validated. The colour retinal images captured from the diabetic patients are enhanced by an effective pre-processor. A bag of features based on intensity, colour and texture are extracted. Finally, the features are classified with the help of partial least square classifier. The classifier performance is validated on two publicly available databanks. The developed method obtains an area under receiver operating characteristic curve of 0.98 with an average execution time of 6 s. This application outperforms the existing approaches with high robustness and efficiency.

Automatic detection of optic disc in color fundus retinal images using circle operator

Abstract In the field of computer aided eye disease diagnosis, automatic optic disk detection is required. In this paper a method is proposed to detect optic disk automatically in color retinal fundus image without using background mask and blood vessels. Based on the properties of optic disk, an idea of circle operator is presented here. This method has been applied on six public databases and the promising results are obtained. The experimental results indicate that this proposed method of automatic optic disk detection has good accuracy and is also time efficient.

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis.

The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.

Retinal vessel segmentation using neural network

Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular diseases. Due to the complex nature of retinal vessel network, the manual segmentation of vessels is a tedious task which also requires high training and skills. This study presents a new method for blood vessel segmentation in colour retinal images using supervised approach. Initially, a set of core features including Gabor filter responses, Frangi's vesselness measure (1D), local binary pattern feature (1D), Hu moment invariants (7D) and grey-level co-occurrence matrix features (3D) are considered. The neural network is trained with the different subsets of core features and it is found that the model with 13D features excluding the Hu moment invariants results in better performance. This model is used for evaluation. The proposed supervised segmentation approach is tested on publicly available structured analysis of the retina, digital retinal images for vessel extraction and CHASE_DB1 databases which contain manually labelled images. The performance of the proposed algorithm is evaluated on the basis of accuracy, sensitivity, specificity and area under the curve. The proposed technique achieves high mean accuracy and sensitivity while it is compared with the several previously proposed algorithms.

Automatic Detection of Blood Vessels in Digital Retinal Images using Soft Computing Technique

Insulin-dependent mortals (humans) are attributed by blemished (defective) metabolism of glycogen (glucose) that leads to long lasting dysfunction and damage of an organ. The most prosaic (common) obstacle (complication) of diabetes is Diabetic Retinopathy, which is one of the predominant (primary) root of visual death and visual impairment in middle aged patient. The expeditious (rapid) mutation of diabetes patient thrust the limitations of the present Diabetic Retinopathy Screening potential for which the digital visualize of eye ball fundus can provide a potential solution by an automated image analysis algorithm. The present learning focus is developing the extraction of normal and isolated characteristics or marks in colour retinal images. The adaptive filters are tuned to match the lump (part) of vessel to be extracted in green channel images. To classify the pixels into vessels and non-vessels the Biogeography Based Optimization Algorithm is applied.

Dust Particle Artifact Detection and Removal in Retinal Images

Retinal fundus cameras suffer from dust particles attaching to the sensor and lens, which manifest as small artifacts on the images. We propose a new strategy for the detection and removal of dust particle artifacts in retinal images. We consider as input two or more color fundus images acquired within the same session, in which we assume the artifacts remain in the same position. Our method consists in detecting candidate artifacts via normalized cross correlation with an artifact template, performing segmentation via region growing, and comparing the segmentations in all images. This guarantees that all detections are consistent for all images. The removal stage consists in an inpainting procedure so that the new region does not stand out from the neighboring regions. Encouraging experimental results show the localization of artifacts is effective and the artifacts are successfully removed, while not introducing new artifacts in the color retinal images.

EFFICIENT DETECTION OF MICRO-ANEURYSMS IN RETINAL IMAGES FOR EARLY DIAGNOSIS OF DIABETIC RETINOPATHY

Diabetic retinopathy (DR) is a serious complication of diabetes mellitus and one of the major causes of blindness worldwide. As the number of diabetic patients increases, early detection of DR for regular screening can prevent loss of vision and blindness. The development of algorithms for detecting dark lesions may turn very useful in the early diagnosis and screening of retinopathy diseases. In this paper, a novel hybrid algorithm for the microaneurysms (MAs) detection is developed. This task is based on mathematical morphology, which is followed by a classification step. Although some algorithms have been developed, the accurate detection of MAs in color retinal images is still a challenging problem. The proposed approach aims to increase the number of true positives and minimize the false positives compared with methods developed in the literature. The new approach is tested on a set of 219 ophthalmologic images. A total of 12 microaneurysm features are considered in this study and selected for KNN classification. The validity of detection process is checked through comparisons at the pixel level with ophthalmologists’ hand-drawn ground truth. Sensitivity, specificity, prediction rate, and accuracy achieved by the proposed approach are 98.13%, 99.71%, 99.63% and 99.01% respectively.

Automated System for Detection of High Risk Diabetic Macular Edema in Color Retinal Images

Automated System for Detection of High Risk Diabetic Macular Edema in Color Retinal Images

Computer-aided diagnosis based on enhancement of degraded fundus photographs.

Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0-1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis.

End-to-End Adversarial Retinal Image Synthesis.

In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

Deep tessellated retinal image detection using Convolutional Neural Networks.

Tessellation in fundus is not only a visible feature for aged-related and myopic maculopathy but also confuse retinal vessel segmentation. The detection of tessellated images is an inevitable processing in retinal image analysis. In this work, we propose a model using convolutional neural network for detecting tessellated images. The input to the model is pre-processed fundus image, and the output indicate whether this photograph has tessellation or not. A database with 12,000 colour retinal images is collected to evaluate the classification performance. The best tessellation classifier achieves accuracy of 97.73% and AUC value of 0.9659 using pretrained GoogLeNet and transfer learning technique.