Digital Retinal Fundus Images Research Articles

Implementation of Deep Learning based Automated Diagnosis of Glaucoma Using Digital Retinal Fundus Images

Glaucoma is a prevalent chronic condition that can cause irreversible vision loss. The number of individuals sufferingfrom permanent vision loss as a result of glaucoma is predicted to rise at an alarming rate in the near future. Thereis a lot of study being done on computer-aided diagnosis for glaucoma. The optic cup (OC) and optic disc (OD) aretypically segmented in retinal fundus images to distinguish between glaucomatous and non-glaucomatous instances.However, the OC boundaries are quite non-distinctive; as a result, accurate OC segmentation is extremely difficult,and OD segmentation performance also needs to be improved. To address this issue, we suggest two networks foraccurate glaucoma screening: CNN and RNN-LSTM. We created a CNN-RNN hybrid that extracts not only the spatialinformation in a fundus image but also the temporal features encoded in fundus sequential images. A CNN and acombined CNN and Long Short-Term Memory RNN were trained using 1810 fundus pictures and 295 fundus videos.In differentiating glaucoma from healthy eyes, the combined CNN/RNN model achieved an average F-measure of95.2%. In comparison, the fundamental CNN model only achieved an average F-measure of 78.2%. Both proposednetworks include a separable convolutional link to improve computational efficiency and lower network costs. Theproposed architecture can provide great accuracy even with only a few trainable parameters.

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods.

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

Diagnostic Accuracy of Digital Retinal Fundus Image Analysis in Detecting Diabetic Maculopathy in Type 2 Diabetes Mellitus

Background: Digital retinal imaging is the gold standard technique for diabetic retinopathy (DR) and diabetic macular oedema (DME) assessment during DR screening. Objectives: To evaluate the diagnostic accuracy of digital retinal fundus image (DRFI) analysis in detecting DME using three manual grading systems (MGS) and comparing it with optical coherence tomography (OCT) findings. Method: A total of 287 DRFI of 287 eyes were analysed. Non-stereoscopic 45° images were acquired using a Kowa VX-20 camera and were graded according to three MGS: Early Treatment Diabetic Retinopathy Study (ETDRS), International Clinical Diabetic Retinopathy (ICDR), and United Kingdom National Screening Committee (UKNSC). The two graders were masked to the patient’s clinical DR status. DME characteristics were analysed using OCTs. Results: A very good agreement in detecting DME was found with Cohen’s κ = 0.83 (ICDR vs. ETDRS), κ = 0.83 (ICDR vs. UKNSC), and κ = 0.82 (ETDRS vs. UKNSC). Sensitivity and specificity of DRFI analysis in DME assessment were 70.0 and 69.6% for UKNSC, 71.9 and 67.4% for ETDRS, and 70.9 and 65.2% for ICDR, respectively. Positive and negative predictive values were 91.7 and 32.7% for UKNSC, 91.4 and 33.3% for ETDRS, and 90.7 and 31.9% for ICDR, respectively. On OCT scans, micro-architectural damages of both inner and outer retinal layers and mean ganglion cell layer thickness showed a significant association with the presence of DME detected with DRFI analysis. Conclusions: Despite the low negative predictive value, the good specificity and sensitivity of DRFI in detecting DME make it a useful tool in a routine clinical setting, and its potential in diabetic eye screening is yet to be realized.

Exporting Diabetic Retinopathy Images from VA VistA Imaging for Research.

The US Department of Veterans Affairs has been acquiring store and forward digital diabetic retinopathy surveillance retinal fundus images for remote reading since 2007. There are 900+ retinal cameras at 756 acquisition sites. These images are manually read remotely at 134 sites. A total of 2.1 million studies have been performed in the teleretinal imaging program. The human workload for reading images is rapidly growing. It would be ideal to develop an automated computer algorithm that detects multiple eye diseases as this would help standardize interpretations and improve efficiency of the image readers. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs have been developed and there are needs for additional image data to validate this work. To further this research, the Atlanta VA Health Care System (VAHCS) has extracted 112,000 DICOM diabetic retinopathy surveillance images (13,000 studies) that can be subsequently used for the validation of automated algorithms. An extensive amount of associated clinical information was added to the DICOM header of each exported image to facilitate correlation of the image with the patient's medical condition. The clinical information was saved as a JSON object and stored in a single Unlimited Text (VR = UT) DICOM data element. This paper describes the methodology used for this project and the results of applying this methodology.

Automated DR and prediction of various related diseases of retinal fundus images

An Automated DR system is developed and prediction of various related diseases are analyzed. Digital Retinal Fundus image is analyzed for the classification of various stages of Diabetic Retinopathy (DR). This imparts much importance, since many of systemic organs disease are related to the developmental stages of DR and those could be predicted well in advance, by analyzing the fundus image itself which is a cost effective technique. This kind of cost effective automated system is needed for mass screening programs in the developing nations. The system is tested on the database obtained from a private hospital in Tamilnadu, which consists of 196 images of different diagnoses. The system outputs are validated with the Expert ophthalmologist’s ground truth images. The proposed method involves three main phases. In the first phase is preprocessing steps are done followed by next phase of removal of normal features with extraction of abnormal features and its statistical values. The third phase is classification stages which includes mild, moderate to severe, severe PDR and neovascularization etc. using neural network. The proposed method has achieved a sensitivity, specificity and accuracy as 94.87%, 90% and 93.22%. The automated detection and classification method surely gives an important and promising data and information to the doctors for further progress in the treatment.

Automated segmentation of foveal avascular zone in digital colour retinal fundus images

Automated segmentation of foveal avascular zone in digital colour retinal fundus images

Automated segmentation of foveal avascular zone in digital colour retinal fundus images

One of the diabetes complications, diabetic retinopathy (DR), is characterised by the damage of retinal vessels, especially on the macular region. Located at the centre of retina and appeared as a cloudy dark spot in colour fundus image, macula is a fundamental area for high acumen of colour vision. Foveal avascular zone (FAZ) is located at the centre of macula and encircled by interconnected capillary beds. FAZ has a round or oval shape with an average diameter of 500-600 μm. In DR patients, the FAZ becomes larger due to the loss of perifoveal retinal vessels. In this study, a scheme for automated segmentation of FAZ in colour fundus images is proposed. The scheme consists of four stages: pre-processing, image enhancement, vessels segmentation and FAZ segmentation. Result shows that the average sensitivity, specificity and accuracy obtained are 80.86%, 99.17% and 97.49%. This indicates that the proposed scheme has successfully detected the FAZ.

Detection of hard exudates using 2D Otsu algorithm in digital retinal fundus image

Blindness is one of the most serious complications among diabetic patients due to diabetic retinopathy. Primary cause of diabetic retinopathy is abnormality of retinal blood vessels which becomes fragile and often raptured, due to which lipid deposits gets accumulated in the intra retinal space and are visible as yellowish colour in varying shapes sizes and locations. This paper presents a method which uses 2D Otsu thresholding to detect the presence of exudates in digital retinal fundus image. The proposed method uses various techniques, like Hough transform, image thresholding etc. and exhibits a sensitivity of 97.60%, specificity of 98.15% and accuracy of 97.85%.

A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.

Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state. Also the diagnosis of diseases did not employ machine learning methods to categorize candidate fundus images into true positive and true negative ratio. Several candidate fundus images did not include more detailed feature selection technique for diabetic retinopathy. To apply machine learning methods and classify the candidate fundus images on the basis of sliding window a method called, Diabetic Fundus Image Recuperation (DFIR) is designed in this paper. The initial phase of DFIR method select the feature of optic cup in digital retinal fundus images based on Sliding Window Approach. With this, the disease state for diabetic retinopathy is assessed. The feature selection in DFIR method uses collection of sliding windows to obtain the features based on the histogram value. The histogram based feature selection with the aid of Group Sparsity Non-overlapping function provides more detailed information of features. Using Support Vector Model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy diseases. The ranking of disease level for each candidate set provides a much promising result for developing practically automated diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, specificity rate, ranking efficiency and feature selection time.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.

An Efficient Method to Detect Diabetic Retinopathy Using Gaussian-Bilateral and Haar Filters with Threshold Based Image Segmentation

A digital imaging technique is utilized in almost all the fields. Based on image processing concept image particle shape can be analyzed in detail. Nowadays, in eye clinics, imaging of the eye fundus with modern technology is in high demand because of its worth and expected lifetime. Eye fundus imaging is considered a non- invasive and painless route to screen and monitor the micro vascular distinction of diabetes and diabetic retinopathy. In general, Optic Disc (OD) signifies the creation of the optic nerve. It is the point where the axons of retinal ganglion cells gain nearer. The Optic Disc is an access point of major blood vessels which provides the retina. In this study a method is introduced to automatically detect the position of the OD in digital retinal fundus images. The OD detection algorithm is based on the identical expected directional pattern of the retinal blood vessels. In this study two types of filters are proposed, one is Gaussian based bilateral filter, to reduce/eliminate the noise of the fundus images and another is a Haar filter to detect the diabetic retinopathy in the fundus images. The most excellent method to segment the images is thresholding based connected component pixels. The results have been taken from many diabetic retinopathy images. In this study for implementation efficient image filtering was used and named as OpenCV 2.4.9.0 and cvblobslib to accomplish successful result. In future development, the fovea detection will be applied.

Computer assisted evaluation of retinal vessels tortuosity in Fabry disease

Fabry Disease (FD) is a rare X-linked metabolic disorder characterized by diffuse deposition of sphingolipids in many tissues. Retinal vessel tortuosity is a common ocular manifestation in FD and may represent a useful marker for the disease. Unfortunately its clinical evaluation is poorly reproducibile and alternative means of evaluation may be of interest. We tested a new semi-automatic software measuring retinal vessel tortuosity from eye fundus digital images in a group of FD patients. Observational case-control study evaluating four mathematical parameters describing tortuosity (relative length, sum of angle metric [SOAM], product of angle distance [PAD], triangular index) obtained from fundus pictures of 35 FD patients and 35 age-matched controls. Only the right eye was considered in order to reduce bias. Mann-Whitney test was used to compare the FD group versus the control group, males versus females and patients with versus without clinically identified retinal vessels tortuosity in the FD group. Linear regression analysis was performed on a subgroup of patients to evaluate the possible association of retinal vessels tortuosity parameters with age and with markers of systemic disease's progression. Three parameters (SOAM, PAD and triangular index) were significantly higher in FD patients in comparison with the controls (p < 0.0001, p = 0.001, p = 0.002 respectively). In the FD group the same three parameters showed higher values in hemizygous males than in heterozygous females ((p < 0.0001, p = 0.002, p < 0.0001 respectively). A computer assisted analysis of retinal vasculature demonstrated an increased vessels tortuosity in FD patients. The technique might be useful to establish disease severity and monitor its progression.

Automatic detection of age-related macular degeneration pathologies in retinal fundus images

Advanced techniques in image processing and analysis are being extensively studied to assist clinical diagnoses. Digital colour retinal fundus images are widely utilised to investigate various eye diseases. In this paper, we describe the detection of optic disc (OD), macula and age-related macular degeneration (ARMD) pathologies of the macular regions in colour fundus images. ARMD causes the loss of central vision in older adults. If the disease is detected early and treated promptly, much of the vision loss can be prevented. Eighty colour retinal fundus images were tested using our proposed algorithm. The Hough transform was employed for OD determination. A fundus coordinate system was established based on the macula location. An ARMD pathology detection methodology using a subtraction process after contrast-limited adaptive histogram equalisation operations was proposed. The accuracies of the automated segmentations of the OD, macula and ARMD pathologies obtained were 100%, 100% and 95.49%, respectively. These results show that our algorithm is a useful tool for detecting ARMD in retinal fundus images. The application of our method may reduce the time needed by ophthalmologists to diagnose ARMD pathology while providing dependable detection precision. Integration of our technique into traditional software could be used in clinical implementations as an aid in disease diagnosis and as a tool for quantitative evaluation of treatment effectiveness.

Retinal vasculature enhancement using independent component analysis

Retinal vasculature is a network of vessels in the retinal layer. In ophthalmology, information of retinal vasculature in analyzing fundus images is important for early detection of diseases related to the retina, e.g. diabetic retinopathy. However, in fundus images the contrast between retinal vasculature and the background is very low. As a result, analyzing or visualizing tiny retinal vasculature is difficult. There-fore, enhancement of retinal vasculature in digital fundus image is important to provide better visualization of retinal blood vessels as well as to increase accuracy of retinal vasculature segmentation. Fluorescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this research work, the low contrast problem of retinal fundus images ob-tained from fundus camera is addressed. We develop a fundus image model based on probability distribution function of melanin, haemoglobin and macular pigment to represent melanin, retinal vasculature and macular region, respectively. We determine retinal pigments makeup, namely macular pigment, melanin and haemoglobin using independent component analysis. Independent component image due to haemoglobin obtained is used since it exhibits higher contrast retinal vasculature. Contrast of reti-nal vasculature from independent component image due to haemoglobin is compared to those from other enhancement methods. Results show that this approach outperforms other non-invasive enhancement methods, such as contrast stretching, histogram equalization and CLAHE and can be beneficial for retinal vasculature segmentation. Contrast enhancement factor up to 2.62 for a digital retinal fundus image model is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.

Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter

Optic disc (OD) detection is a main step while developing automated screening systems for diabetic retinopathy. We present in this paper a method to automatically detect the position of the OD in digital retinal fundus images. The method starts by normalizing luminosity and contrast through out the image using illumination equalization and adaptive histogram equalization methods respectively. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Hence, a simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity. The retinal vessels are segmented using a simple and standard 2-D Gaussian matched filter. Consequently, a vessels direction map of the segmented retinal vessels is obtained using the same segmentation algorithm. The segmented vessels are then thinned, and filtered using local intensity, to represent finally the OD-center candidates. The difference between the proposed matched filter resized into four different sizes, and the vessels' directions at the surrounding area of each of the OD-center candidates is measured. The minimum difference provides an estimate of the OD-center coordinates. The proposed method was evaluated using a subset of the STARE project's dataset, containing 81 fundus images of both normal and diseased retinas, and initially used by literature OD detection methods. The OD-center was detected correctly in 80 out of the 81 images (98.77%). In addition, the OD-center was detected correctly in all of the 40 images (100%) using the publicly available DRIVE dataset.