Manuel G. Penedo

Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images

Analysis of retinal vessel tree characteristics is an important task in medical diagnosis, specially in cases of diseases like vessel occlusion, hypertension or diabetes. The detection and classification of feature points in the arteriovenous eye tree will increase the information about the structure allowing its use for medical diagnosis. In this work a method for detection and classification of retinal vessel tree feature points is presented. The method applies and combines imaging techniques such as filters or morphologic operations to obtain an adequate structure for the detection. Classification is performed by analysing the feature points environment. Detection and classification of feature points is validated using the VARIA database. Experimental results are compared to previous approaches showing a much higher specificity in the characterisation of feature points while slightly increasing the sensitivity. These results provide a more reliable methodology for retinal structure analysis.

Retinal verification using a feature points-based biometric pattern

Biometrics refer to identity verification of individuals based on some physiologic or behavioural characteristics. The typical authentication process of a person consists in extracting a biometric pattern of him/her and matching it with the stored pattern for the authorised user obtaining a similarity value between patterns. In this work an efficient method for persons authentication is showed. The biometric pattern of the system is a set of feature points representing landmarks in the retinal vessel tree. The pattern extraction and matching is described. Also, a deep analysis of similarity metrics performance is presented for the biometric system. A database with samples of retina images from users on different moments of time is used, thus simulating a hard and real environment of verification. Even in this scenario, the system allows to establish a wide confidence band for the metric threshold where no errors are obtained for training and test sets.

Retinal vessel tree segmentation using a deformable contour model

This paper presents an improved version of our specific methodology to detect the vessel tree in retinal angiographies. The automatic analysis of retinal vessel tree facilitates the computation of the arteriovenous index, which is essential for the diagnosis several eye diseases. The developed system is inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties. It profits from the automatic localization of the optic disc, the vessel creases extraction and, as a recent innovation, the morphological vessel segmentation, all developed in our research group. After researching and testing our system, the parameter configuration has been enhanced. Significantly better results in the detection of arteriovenous structures are obtained, keeping a high efficiency, as shown by the systems performance evaluation on the publicly available DRIVE database.

Sirius: A web-based system for retinal image analysis

PURPOSE Retinal image analysis can lead to early detection of several pathologies such as hypertension or diabetes. Screening processes require the evaluation of a high amount of visual data and, usually, the collaboration between different experts and different health care centers. These usual routines demand new fast and automatic solutions to deal with these situations. This work introduces Sirius (System for the Integration of Retinal Images Understanding Services), a web-based system for image analysis in the retinal imaging field. METHODS Sirius provides a framework for ophthalmologists or other experts in the field to collaboratively work using retinal image-based applications in a distributed, fast and reliable environment. Sirius consists of three main components: the web client that users interact with, the web application server that processes all client requests and the service module that performs the image processing tasks. In this work, we present a service for the analysis of retinal microcirculation using a semi-automatic methodology for the computation of the arteriolar-to-venular ratio (AVR). RESULTS Sirius has been evaluated in different real environments, involving health care systems, to test its performance. First, the AVR service was validated in terms of precision and efficiency and then, the framework was evaluated in different real scenarios of medical centers. CONCLUSIONS Sirius is a web-based application providing a fast and reliable work environment for retinal experts. The system allows the sharing of images and processed results between remote computers and provides automated methods to diminish inter-expert variability in the analysis of the images.

Improving retinal artery and vein classification by means of a minimal path approach

This paper describes a technique for the retinal vessel classification into artery and vein categories from fundus images within a framework to compute the arteriovenous ratio. This measure is used to assess the patient condition, mainly in hypertension and it is computed as the ratio between artery and vein widths. To this end, the vessels are segmented and measured in several circumferences concentric to the optic nerve. The resulting vessel segments at each radius are classified as artery or vein independently. After that, a tracking procedure joins vessel segments in different radii that belong to the same vessel. Finally, a voting system is applied to obtain the final class of the whole vessel. The methodology has been tested in a data set of 100 images labeled manually by two medical experts and a classification rate of over 87.68 % has been obtained.

Personal verification based on extraction and characterisation of retinal feature points

This paper describes a methodology of verification of individuals based on a retinal biometric pattern. The pattern consists in feature points of the retinal vessel tree, namely bifurcations and crossovers. These landmarks are detected and characterised adding semantic information to the biometric pattern. The typical authentication process of a person once extracted the biometric pattern includes matching it with the stored pattern for the authorised user obtaining a similarity value between them. A matching algorithm and a deep analysis of similarity metrics performance is presented. The semantic information added for the feature points allows to reduce the computation load in the matching process as only points classified equally can be matched. The system is capable of establishing a safe confidence band in the similarity measure space between scores for patterns of the same individual and between different individuals.

Genetic approaches for topological active nets optimization

The topological active nets (TANs) model is a deformable model used for image segmentation. It integrates features of region-based and edge-based segmentation techniques so it is able to fit the contours of the objects and model their inner topology. Also, topological changes in its structure allow the detection of concave and convex contours, holes, and several objects in the scene. Since the model deformation is based on the minimization of an energy functional, the adjustment depends on the minimization algorithm. This paper presents two evolutionary approaches to the energy minimization problem in the TAN model. The first proposal is a genetic algorithm with ad hoc operators whereas the second approach is a hybrid model that combines genetic and greedy algorithms. Both evolutionary approaches improve the accuracy of the segmentation even though only the hybrid model allows topological changes in the model structure.

On the Automatic Computation of the Arterio-Venous Ratio in Retinal Images: Using Minimal Paths for the Artery/Vein Classification

Abnormalities in the retinal vessel tree are associated with different pathologies. Usually, they affect arteries and veins differently. In this regard, the arteriovenous ratio(AVR) is a measure of retinal vessel caliber, widely used in medicine to study the influence of these irregularities in disease evolution. Hence, the development of an automatic tool for AVR computation as well as any other tool for diagnosis support need an objective, reliable and fast artery/vein classifier. This paper proposes a technique to improve the retinal vessel classification in an AVR computation framework. The proposed methodology combines a color clustering strategy and a vessel tracking procedure based on minimal path approaches. The tests performed with 58 images manually labeled by three experts show promising results.

Localisation of the optic disc by means of GA-optimised Topological Active Nets

In this paper we propose a new approach to the optic disc localisation process in digital retinal images by means of Topological Active Nets (TAN). This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. In this paper the active nets incorporate new energy terms for the optic disc localisation and their optimisation is performed with a genetic algorithm, with adapted or new ad hoc genetic operators. There is no need of any pre-processing of the images, which allows a quasi automatic localisation of the optic disc. This process also provides a simultaneous segmentation of the disc. We present representative results of optic disc localisations showing the advantages of the approach, with images focusing on the optic disc or on the macula, and with images with different levels of noise and lesion areas.

Localization and extraction of the optic disc using the fuzzy circular hough transform

This paper presents an algorithm for automatic extraction of the optic disc in retinal images. The developed system consists of two main parts. Firstly, the localization of the region containing the optic disc is performed by means of a clustering algorithm. Then, in order to extract the optic disc, the fuzzy circular Hough transform is applied to the edges of the region. The optic disc might not be extracted since there are vessels in the inside of the optic disc. To avoid this, a crease extraction algorithm is applied to the retinal image. The vessels are extracted and the vessel edge points contained in the edge image are removed. The final system was tested by ophthalmologists. The localization of the region of interest is correct in 100% of the cases and the extraction of the optic disc is obtained in 98% of the cases.