Human Visual Perception and Retinal Diseases

Retinal diseases are causing alterations of the visual perception leading sometimes to blindness. For this reason, early detection and diagnosis of retinal pathologies is very important. Using digital image processing techniques, retinal images may be analyzed quickly and computer-assisted diagnosis systems may be developed in order to help the ophthalmologists to make a diagnosis. In this paper we described shortly two computer-assisted systems for the detection of retinal landmarks (optic disc and vasculature) together with a brief introduction to the human visual system and to some alterations of the visual perception caused by retinal diseases

Analysis of normal human retinal vascular network architecture using multifractal geometry

AIM: To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina. METHODS: Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyses were performed using the GraphPad InStat software. RESULTS: The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions (Dq) for q=0, 1, 2, the width of the multifractal spectrum (Δα=αmax - αmin) and the spectrum arms’ heights difference (│Δf│) of the normal images were expressed as mean±standard deviation (SD): for segmented versions, D0=1.7014±0.0057; D1=1.6507±0.0058; D2=1.5772±0.0059; Δα=0.92441±0.0085; │Δf│= 0.1453±0.0051; for skeletonised versions, D0=1.6303±0.0051; D1=1.6012±0.0059; D2=1.5531± 0.0058; Δα=0.65032±0.0162; │Δf│= 0.0238±0.0161. The average of generalized dimensions (Dq) for q=0, 1, 2, the width of the multifractal spectrum (Δα) and the spectrum arms’ heights difference (│Δf│) of the segmented versions was slightly greater than the skeletonised versions. CONCLUSION: The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases

Automatic Unsupervised Segmentation of Retinal Vessels using Self-Organizing Maps and K-means clustering

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A self-organizing map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the self-organizing map, and the class of each pixel will be the class of the best matching unit on the self-organizing map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segmentation and the ground truth. The mean accuracy is 0.9459 with a standard deviation of 0.0094 is outperforming the manual segmentation rates obtained by other widely used unsupervised methods. A good kappa value of 0.6562 is inline with state-of-the-art supervised and unsupervised approaches

Graph-Based Minimal Path Tracking in the Skeleton of the Retinal Vascular Network

This paper presents a semi-automatic framework for minimal path tracking in the skeleton of the retinal vascular network. The method is based on the graph structure of the vessel network. The vascular network is represented based on the skeleton of the available segmented vessels and using an undirected graph. Significant points on the skeleton are considered nodes of the graph, while the edge of the graph is represented by the vessel segment linking two neighboring nodes. The graph is represented then in the form of a connectivity matrix, using a novel method for defining vertex connectivity. Dijkstra and Floyd-Warshall algorithms are applied for detection of minimal paths within the graph. The major contribution of this work is the accurate detection of significant points and the novel definition of vertex connectivity based on a new neighborhood system adopted. The qualitative performance of our method evaluated on the publicly available DRIVE database shows useful results for further purposes

Approximated overlap error for the evaluation of feature descriptors on 3D scenes

This paper presents a new framework to evaluate feature descriptors on 3D datasets. The proposed method employs the approximated overlap error in order to conform with the reference planar evaluation case of the Oxford dataset based on the overlap error. The method takes into account not only the keypoint centre but also the feature shape and it does not require complex data setups, depth maps or an accurate camera calibration. Only a ground-truth fundamental matrix should be computed, so that the dataset can be freely extended by adding further images. The proposed approach is robust to false positives occurring in the evaluation process, which do not introduce any relevant changes in the results, so that the framework can be used unsupervised. Furthermore, the method has no loss in recall, which can be unsuitable for testing descriptors. The proposed evaluation compares on the SIFT and GLOH descriptors, used as references, and the recent state-of-the-art LIOP and MROGH descriptors, so that further insight on their behaviour in 3D scenes is provided as contribution too

Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas

In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region

FABC:retinal vessel segmentation using AdaBoost

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE.Training was conducted confined to the dedicated training set from the DRIVE database, and feature-basedAdaBoost classifier (FABC)was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy (0.9597 versus 0.9473 for the nearest performer)

Semi-automatic registration of retinal images based on line matching approach

Accurate retinal image registration is essential to track the evolution of eye-related diseases. We propose a semiautomatic method based on features relying upon retinal graphs for temporal registration of retinal images. The features represent straight lines connecting vascular landmarks on the retina vascular tree: bifurcations, branchings, crossings, end points. In the built retinal graph, one straight line between two vascular landmarks indicates that they are connected by a vascular segment in the original retinal image. The locations of the landmarks are manually extracted to avoid the information loss due to errors in a retinal vessels segmentation algorithms. A straight line model is designed to compute a similarity measure to quantify the line matching between images. From the set of matching lines, corresponding points are extracted and a global transformation is computed. The performance of the registration method is evaluated in the absence of ground truth using the cumulative inverse consistency error (CICE)

An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons

Full-scale morphologically and biophysically realistic model networks, aiming at modeling multiple brain areas, provide an invaluable tool to make significant scientific advances from in-silico experiments on cognitive functions to digital twin implementations. Due to the current technical limitations of supercomputer systems in terms of computational power and memory requirements, these networks must be implemented using (at least) simplified neurons. A class of models which achieve a reasonable compromise between accuracy and computational efficiency is given by generalized leaky integrate-and fire models complemented by suitable initial and update conditions. However, we found that these models cannot reproduce the complex and highly variable firing dynamics exhibited by neurons in several brain regions, such as the hippocampus. In this work, we propose an adaptive generalized leaky integrate-and-fire model for hippocampal CA1 neurons and interneurons, in which the nonlinear nature of the firing dynamics is successfully reproduced by linear ordinary differential equations equipped with nonlinear and more realistic initial and update conditions after each spike event, which strictly depends on the external stimulation current. A mathematical analysis of the equilibria stability as well as the monotonicity properties of the analytical solution for the membrane potential allowed (i) to determine general constraints on model parameters, reducing the computational cost of an optimization procedure based on spike times in response to a set of constant currents injections; (ii) to identify additional constraints to quantitatively reproduce and predict the experimental traces from 85 neurons and interneurons in response to any stimulation protocol using constant and piecewise constant current injections. Finally, this approach allows to easily implement a procedure to create infinite copies of neurons with mathematically controlled firing properties, statistically indistinguishable from experiments, to better reproduce the full range and variability of the firing scenarios observed in a real network