Infrastructure for Retinal Image Analysis

This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features

Intensity-based Choroidal Registration Using Regularized Block Matching

Detecting and monitoring changes in the human choroid play a crucial role in treating ocular diseases such as myopia. However, reliable segmentation of optical coherence tomography (OCT) images at the choroid-sclera interface (CSI) is notoriously difficult due to poor contrast, signal loss and OCT artefacts. In this paper we present blockwise registration of successive scans to improve stability also during complete loss of the CSI-signal. First, we formulated the problem as minimization of a regularized energy functional. Then, we tested our automated method for piecewise Intensity-based Choroidal rigid Registration using regularized block matching (ICR) on 20 OCT 3D-volume scan-rescan data set pairs. Finally, we used these data set pairs to determine the precision of our method, while the accuracy was determined by comparing our results with those using manually annotated scans

A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images

oai:omia:id:27639Qualitative evaluation of stereo retinal fundus images by experts is a widely accepted method for optic nerve head evaluation (ONH) in glaucoma. The quantitative evaluation using stereo involves depth estimation of the ONH and thresholding of depth to extract optic cup. In this paper, we attempt the reverse, by estimating the disc depth using supervised and unsupervised techniques on a single optic disc image. Our depth estimation approach is evaluated on the INSPIRE-stereo dataset by using single images from the stereo pairs, and is compared with the OCT based depth ground truths. We extract spatial and intensity features from the depth maps, and perform classification of images into glaucomatous and normal. Our approach is evaluated on a dataset of 100 images and achieves an AUC of 0.888 with a sensitivity of 83% at specificity 83%. Experiments indicate that our approach can reliably estimate depth, and provide valuable information for glaucoma detection and for monitoring its progression

Automated Morphometric Analysis of in-vivo Human Corneal Endothelium

In-vivo specular and confocal microscopy provide information on the corneal endothelium health state. The reliable estimation of the clinical parameters requires the accurate detection of cell contours. We propose a method for the automatic segmentation of cell contour. The centers of the cells are detected by convolving the original image with Laplacian of Gaussian kernels, whose scales are set according to the cell size preliminary estimated through a frequency analysis. A structure made by connected vertices is derived from the centers, and it is fine-tuned by combining information about the typical regularity of endothelial cells shape with the pixels intensity of the actual image. Ground truth values for the clinical parameters were obtained from manually drawn cell contours. An accurate automatic estimation is achieved on 30 images: for each clinical parameter, the mean difference between its manual estimation and the automated one is always less than 7%

Image Quality Classification for DR Screening Using Convolutional Neural Networks

The quality of input images significantly affects the outcome of automated diabetic retinopathy screening systems. Current methods to identify image quality rely on hand-crafted geometric and structural features, that does not generalize well. We propose a new method for retinal image quality classification (IQC) that uses computational algorithms imitating the working of the human visual systems. The proposed method leverages on learned supervised information using convolutional neural networks (CNN), thus avoiding hand-engineered features. Our analysis shows that the learned features capture both geometric and structural information relevant for image quality classification. Experimental results conducted on a relatively large dataset demonstrates that the overall method can achieve high accuracy. We also show that effective features for IQC can be learned by full training of shallow CNN as well as by using transfer learning

A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema

Diabetic macular edema is one of the leading causes of legal blindness worldwide. Early, and accessible, detection of ophthalmological diseases is especially important in developing countries, where there are major limitations to access to specialized medical diagnosis and treatment. Deep learning models, such as deep convolutional neural networks have shown great success in different computer vision tasks. In medical images they have been also applied with great success. The present paper presents a novel strategy based on convolutional neural networks to combine exudates localization and eye fundus images for automatic classification of diabetic macular edema as a support for diabetic retinopathy diagnosis

Evaluation of the Areas Involved in Visual Cortex in Parkinson's Disease Using Diffusion Tensor Imaging

Parkinson's disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. In PD patients and in primates with experimental Parkinsonism indicating that retinal dopamine deficiency is an important factor in the pathogenesis of PD visual dysfunction. Visual signs and symptoms of PD may include defects in eye movement, pupillary function, and in more complex visual tasks. In this study, we evaluated the areas involved in visual cortex in PD by diffusion tensor imaging to assess the structural change in PD

Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance properties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and inflexible image analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application

Diabetic Macular Edema Grading Based on Deep Neural Networks

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach

Automated Tessellated Fundus Detection in Color Fundus Images

In this work, we propose an automated tessellated fundus detection method by utilizing texture features and color features. Color moments, Local Binary Patterns (LBP), and Histograms of Oriented Gradients (HOG) are extracted to represent the color fundus image. After feature extraction, a SVM classifier is trained to detect the tessellated fundus. Both linear and RBF kernels are applied and compared in this work. A dataset with 836 fundus images is built to evaluate the proposed method. For linear SVM, the mean accuracy of 98% is achieved, with sensitivity of 0.99 and specificity of 0.98. For RBF kernel, the mean accuracy is 97%, with sensitivity of 0.99 and specificity of 0.95. The detection results indicate that color features and texture features are able to describe the tessellated fundus