Optical coherence tomography (OCT) is a non-invasive technique that allows the retina to be studied with precision, the analysis of the features of its layers and other structures such as the macula or the optic nerve. This is why it is used in the diagnosis and monitoring of eye diseases such as glaucoma and optic neuritis. A crucial step in this process is the segmentation of the different layers, which is a great challenge due to its complexity. In this work, a methodology based on deep learning and transfer learning will be developed to automatically segment nine retinal layers in OCT images centred on the optic disc. In addition, the thickness of each retinal layer will be measured along each B-scan. For this purpose, OCT images from a public dataset and a dataset collected from depth-enhanced images will be used. The proposed method achieves a Dice score of 83.6%, similar to that obtained in the state of the art, segmenting the nine retinal layers and the optic disc in both sets of images. In addition, the different layers are represented in three different graphical formats.

Multiple Sclerosis (MS) is a chronic neurological disease, in which immune-mediated mechanisms lead to pathological processes of neurodegeneration. Optical coherence tomography (OCT) has recently begun to be used to diagnose and monitor patients with MS. Morphological changes in the choroid have been linked to the onset of MS, so an accurate segmentation of this layer is critical. Conventional OCT has several limitations in obtaining accurate images of the choroid, which has been improved through the use of systems such as Enhanced Depth Imaging (EDI) OCT. Unfortunately, many longitudinal studies that have collected samples over the years in the past have been performed using highly variable settings and without the use of the EDI protocol (or similar variants). For these reasons, in this work we propose a series of fully automatic approaches, based on convolutional neural networks, capable of robustly segmenting the choroid in OCT images without using the EDI protocol. To test the robustness and efficiency of our method, we performed experiments on a public dataset and a collected one. The Dice score obtained by the best proposed architecture is 89.7 for the public dataset, and 93.7 for the collected dataset.

Abstract Currently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.

Optical Coherence Tomography Angiography (OCTA) images represent the newest ophthalmic image modality with a great potential. They are characterized by allowing the precise non-invasive visualization of the retinal vasculature at different depths and resolutions. Given the relevance of the vascular analysis in this novel image modality, we propose the automatic segmentation and measurement of the existing ischemic areas that represent damaged regions of the eye fundus. In particular, we designed a methodology based in three main steps: image preprocessing, initial segmentation of ischemic regions by using active contours and final segmentation refinement. We obtained satisfactory results in the validation process, with a high separability between defined ischemic degrees in retinal vein occlusion patients. This fully automatic tool, with objectivity and reproducibility, provides an accurate measurement of the ischemic regions in real time, considering an issue where the manual analysis of the specialist is unfeasible in clinical practice routine.

Central serous chorioretinopathy is one of the most frequent causes of vision impairment among middle-aged adults. Optical Coherence Tomography (OCT) is a non-invasive diagnostic technique that is commonly used for the monitoring of this relevant eye disease. In this context, this paper proposes a fully automatic system for the characterization of intraretinal pathological fluid regions associated with central serous chori-oretinopathy using OCT scans. To achieve this, we adapted an end-to-end fully convolutional architecture for semantic pixel-wise segmentation. The proposed methodology was tested using a heterogeneous set of 100 OCT scans of different patients. Satisfactory results were obtained, reaching values of 0.9954±0.0007, 0.8792 ±0.0079 and 0.9651 ±0.0041 for the mean Accuracy, mean Jaccard index and mean Dice coefficient, respectively. The proposed system also demonstrated its competitive performance with respect to other state-of-the-art approaches.

The analysis of remote sensing images represents a highly important issue to be performed in many relevant fields such as climate change studies or land cover mapping. Traditional proposals usually identify the land cover classes from general related groups such as different tree species or different crop varieties. Additionally, these proposals commonly use information from a precise time span or season, not accounting for the variability of the data over the entire year, specially in regions with several seasons. In this work, we propose a multi-temporal classification system to identify and represent diverse land cover classes over any period of the entire year by using Sentinel-2 satellite image data. To this end, 526 representative samples were labelled from 5 complex and variable different land cover types over the Special Area of Conservation (SAC) Betanzos-Mandeo in the northwest of the Iberian Peninsula. The method achieves a satisfactory mean accuracy value of 84.0% for the testing set using the best configuration with a radial Support Vector Machine classifier. This system will be used in the study of the population connectivity of two threatened herptiles, but it can be easily extended to other species of interest in the future.

Optical Coherence Tomography Angiography (OCT-A) represents a new modality of ophthalmological imaging that stands out for being a non-invasive capture technique that facilitates the analysis of the vascular characteristics of the eye fundus. In this paper, we propose a complete automatic methodology that identifies the vascular and avascular zones in OCT-A images, quantifying each one of them for their posterior use in clinical analyses and diagnostic processes. To achieve this, we firstly intensify the vascular characteristics to facilitate the posterior extraction. Then, a set of image processing techniques are combined to differentiate both vascular and avascular regions and, finally, measure their representative parameters. The proposed methodology was tested on a set of images that were marked by an expert ophthalmologist, being used as reference in the validation of the method. The proposed approach presented satisfactory results in the validation experiments with the vascular and avascular measurements, demonstrating their utility for the diagnosis and monitoring of different vascular diseases that are frequently analysed through the retinal microcirculation.

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows the analysis of the eye fundus characteristics in real time. These images enable the experts to make a clinical evaluation of the retinal vasculature, whose morphology provides relevant information for diseases like diabetes, hypertension or arteriosclerosis. In this paper, we present a novel proposal for the automatic vasculature identification in retinal OCT images. To achieve this, we analyse the intensity profiles between representative retinal layers, previously segmented. Then, two statistical models are generated using representative samples of vessel and non-vessel profiles. The analysis of both statistical models let us optimize the discrimination of both cathegories that is used, finally, to identify the vessel locations. The proposed method was adjusted and validated using 256 OCT images, including 1274 vascular structures that were labelled by an expert clinician. Satisfactory results were provided as a precision of 94.55% and a recall of 90.25% were obtained, respectively. The method facilitates the doctors’ work allowing better analysis and treatments of vascular diseases.

Audiology is the science of hearing and auditory processes study. The evaluation of hearing capacity is commonly performed by an audiologist using an audiometer, where the patient is asked to show some kind of sign when he or she recognizes the stimulus. This evaluation becomes much more complicated when the patient suffers some type of cognitive decline that hinders the emission of visible signs of recognition. With this group of patients, a typical question-answer interaction is not applicable, so the audiologist must focus his attention on the patient's spontaneous gestural reactions. This manual evaluation entails a number of problems: it is highly subjective, difficult to determine in real time (since the expert must pay attention simultaneously to the audiological process and the patient's reactions), etc. Considering this, in this paper, we present an automatic methodology for processing video sequences recorded during the performance of the hearing test in order to assist the audiologist in the detection of these spontaneous reactions. This screening method analyzes the movements that occur within the eye area, which has been pointed out by the audiologists as the most representative for these patients. By the analysis of these movements, the system helps the audiologist to determine when a positive gestural reaction has taken place increasing the objectivity and reproducibility.

Dry eye syndrome is recognized as a growing health problem, and one of the most frequent reasons for seeking eye care. Its etiology and management challenge clinicians and researchers alike, and several clinical tests can be used to diagnose it. One of the most frequently used tests is the evaluation of the interference patterns of the tear film lipid layer. Based on this clinical test, this paper presents CASDES, a computer-aided system to support the diagnosis of dry eye syndrome. Furthermore, CASDES is also useful to support the diagnosis of other eye diseases, such as meibomian gland dysfunction, since it provides a tear film map with highly useful information for eye practitioners. Experiments demonstrate the robustness of this novel tool, which outperforms the previous attempts to create tear film maps and provides reliable results in comparison with the clinicians' annotations. Note that the processing time is noticeably reduced with the proposed method, which will help to promote its clinical use in the diagnosis and treatment of dry eye.

This paper presents a new wavefront propagation method for dealing with the classic Eikonal equation. While classic Dijkstra-like graph-based techniques achieve the solution in O(M log M), they do not approximate the unique physically relevant solution very well. Fast Marching Methods (FMM) were created to efficiently solve the continuous problem. The proposed approximation tries to maintain the complexity, in order to make the algorithm useful in a wide range of contexts. The key idea behind our method is the creation of 'mini wave-fronts', which are combined to propagate the solution. Experimental results show the improvement in the accuracy with respect to the state of the art, while the average computational speed is maintained in O(M log M), similar to the FMM techniques.

Cone-Beam Computer Tomography (CBCT) is a technique for capturing images of the maxillofacial region that provides tissue views with low radiation doses. Dental implant planning and management of endodontic problems are some of its main applications. In this work, we propose an automatic methodology to analyze the volumetric CBCT data in order to segment bone tissues in the mandible. The final goal is the development of a methodology for the automatic analysis of the bone features in CBCT modality.

Dry eye syndrome (DES) is a common disorder of the tear film, affecting a significant percentage of the population. The Non Invasive Break-Up Time (NIBUT) is a clinical test used for the diagnosis of DES. In this work, we propose a methodology for the semi-automatic computation of the NIBUT test. We tested our methodology on a dataset composed of 15 videos manually annotated and high success rates were obtained. The promising results of this approach mean a step forward on the development of computer aided tools for DES assessment.

This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before. Thus, a minimal path approach can be used to model human trajectory behaviour. We develop a modified Fast Marching Method that allows us to include both velocity and orientation in the Front Propagation Approach, without increasing its computational complexity. Combining all the information, we create a time surface that shows the time a target need to reach any given position in the scene. We also create different metrics in order to compare the time surface against the real behaviour. Experimental results over a public dataset prove the initial hypothesis' correctness.

Hearing loss is a common disease which affects a large percentage of the population. Hearing loss may have a negative impact on health, social participation, and daily activities, so its diagnosis and monitoring is indeed important. The audiometric tests related to this diagnosis are constrained when the patient suffers from some form of cognitive impairment. In these cases, audilogist must try to detect particular facial reactions that may indicate auditory perception. With the aim of supporting the audiologist in this evaluation, a screening method that analyzes video sequences and seeks for facial reactions within the eye area was proposed. In this research, a comprehensive survey of one of the most relevent steps of this methodology is presented. This survey considers different alternatives for the detection of the interest points and the classsification techniques. The provided results allow to determine the most suitable configuration for this domain.

OCT technique for retinal imaging is establishing itself as a relevant modality among ophthalmologists due to its capacity to show more information than classical modalities. Nowadays, most image processing-based applications are emerging to extract that information automatically. As previous step of any automatic method to extract features from these images, the segmentation of the retinal layers has to be done. Graph-based methods provide good results for this problem, although their efficiency is an important limitation. In this work, a multiscale or pyramidal-based approach is studied in order to solve this problem. Different configurations are proposed to determine the optimal method. It is remarkable that this approach means an improvement not only in computation time, but also in segmentation results.

Dry eye syndrome is characterized by symptoms of discomfort, ocular surface damage, reduced tear film stability, and tear hyperosmolarity. These features can be identified by several types of diagnostic tests, although there may not be a direct correlation between the severity of symptoms and the degree of damage. One of the most used clinical tests is the analysis of the lipid interference patterns, which can be observed on the tear film, and their classification into the Guillon categories. Our previous researches have demonstrated that the interference patterns can be characterized as color texture patterns. Thus, the manual test done by experts can be performed through an automatic process which saves time for experts and provides unbiased results. Nevertheless, the heterogeneity of the tear film makes the classification of a patient's image into a single category impossible. For this reason, this paper presents a methodology to create tear film maps based on the lipid interference patterns. In this way, the output image represents the distribution and prevalence of the Guillon categories on the tear film. The adequacy of the proposed methodology was demonstrated since it achieves reliable results in comparison with the annotations done by experts.

Dry eye is a common disease which affects a large portion of the population and harms their routine activities. Its diagnosis and monitoring require a battery of tests, each designed for different aspects. One of these clinical tests measures the quality of the tear film and is based on its appearance, which can be observed using the Doane interferometer. The manual process done by experts consists of classifying the interferometry images into one of the five categories considered. The variability existing in these images makes necessary the use of an automatic system for supporting dry eye diagnosis. In this research, a methodology to perform this classification automatically is presented. This methodology includes a color and texture analysis of the images, and also the use of feature selection methods to reduce image processing time. The effectiveness of the proposed methodology was demonstrated since it provides unbiased results with classification errors lower than 9%. Additionally, it saves time for experts and can work in real-time for clinical purposes.

Crossovers and bifurcations are interest points of the retinal vascular tree useful to diagnose diseases. Specifically, detecting these interest points and identifying which of them are crossings will give us the opportunity to search for arteriovenous nicking, this is, an alteration of the vessel tree where an artery is crossed by a vein and the former compresses the later. These formations are a clear indicative of hypertension, among other medical problems. There are several studies that have attempted to define an accurate and reliable method to detect and classify these relevant points. In this article, we propose a new method to identify crossovers. Our approach is based on segmenting the vascular tree and analyzing the surrounding area of each interest point. The minimal path between vessel points in this area is computed in order to identify the connected vessel segments and, as a result, to distinguish between bifurcations and crossovers. Our method was tested using retinographies from public databases DRIVE and VICAVR, obtaining an accuracy of 90%.

Dry eye is a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities. Its diagnosis can be achieved by analyzing the interference patterns of the tear film lipid layer and by classifying them into one of the Guillon categories. The manual process done by experts is not only affected by subjective factors but is also very time consuming. In this paper we propose a general methodology to the automatic classification of tear film lipid layer, using color and texture information to characterize the image and feature selection methods to reduce the processing time. The adequacy of the proposed methodology was demonstrated since it achieves classification rates over 97% while maintaining robustness and provides unbiased results. Also, it can be applied in real time, and so allows important time savings for the experts.

In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.

This paper focuses on evaluating a method for measuring the retinal vessel widths in a recently publicly available database (REVIEW). The algorithm is based on deformable models and it constitutes an essential step in a methodology to compute the AVR automatically. This methodology is part of a web-based framework aimed to integrate diverse techniques in the field of retinal image analysis. The results achieved in the REVIEW database indicate the method is comparable or, in some cases, improves the accuracy of other techniques for measuring the vessel widths.

Dry eye is a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities, such as driving or working with computers. Its diagnosis can be achieved by several clinical tests, one of which is the analysis of the interference pattern and its classification into one of the Guillon's categories. The methodologies for automatic classification obtain promising results but at the expense of requiring a long processing time. In this research, feature selection techniques are used to reduce time whilst maintaining performance, paving the way for the development of a novel tool for automatic classification of tear film lipid layer. This tool produces significant classification rates over 96% compared with the annotations of the optometrists and provides unbiased results. Also, it works in real-time and so allows important time savings for the experts.

Dry eye syndrome is a common disorder of the tear film, affecting a significant percentage of the population. The Break-Up Time (BUT) is a clinical test used for the diagnosis of this disease. In this work, we propose several improvements for the automatic computation of the BUT measure that solve the limitations of previous approaches. In particular, we present several procedures for the automatic computation of some parameters involved in the BUT measurement, such as the eye size, the intensity variation, or the starting point of the measurement frame sequence. We have tested our methodology on a dataset composed of 18 videos annotated by 4 different experts. The average difference between the automatic measure and the experts’ measures is on the acceptable range considering the high inter-observer variance.

A new segmentation method using deformable models was defined for medical image segmentation. As deformable model we used Topological Active Nets, model which integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by an Artificial Neural Network (ANN) that learns how to move the nodes of the model based on their energy surrounding. The ANN is applied to each of the nodes and in different temporal steps until the final segmentation is reached. The ANN training is obtained by simulated evolution, using Differential Evolution to automatically obtain the ANN that provides the emergent segmentation. The methodology was adapted and tested in two different medical domains, that is, CT medical images and eye fundus images to demonstrate the potential of the segmentation technique.

In this work we use the evolutionary multiobjective methodology for the optimization of topological active models, a deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. As in other deformable models, a correct segmentation is achieved through the optimization of the model, governed by energy parameters that must be experimentally tuned. Evolutionary multiobjective optimization gives a solution to this problem by considering the optimization of several objectives in parallel. Concretely, we use the SPEA2 algorithm, adapted to our application, the search of the Pareto optimal individuals. The proposed method was tested on several representative images from different domains yielding highly accurate results.

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.

Biometric features have been studied in order to be applied to authentication and identification systems due to its reliability. Among others, the retinal vessel tree have been proposed as a vessel pattern for personal authentication applications, since it is almost impossible to forge. In this kind of systems, the retinal vessel tree is computed from the retinal image, and then a registration process is made. Although reliable and remarkable results have been obtained in this vessel pattern-based system, the required computation effort is quite high, particularly to compute and extract the vessel tree. In this paper, a pixel parallel approach is proposed to tackle with the retinal vessel tree extraction to be used in a personal retinal authentication system, regarding the computation speed.

In this work a new methodology for automatic three dimensional shape reconstruction of bones from tomographic cross-sections is presented, based in the topological active net model. The methodology presented is fundamentaly composed by two steps. In the first one, with the purpose of differentiate the internal and the external contours of bony structures, the existent bones in the first image of the sequence are extracted. For this purpose, a topological analysis of the mesh is done. In the second step the 3D bones' reconstruction is obtained by tracking the active net in the rest of slices using the information obtained in previous stages of the tracking process and introducing new energy terms based in directional filters.

In this work, we have developed a computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture. This was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs. The first ANN performs the detection of suspicious regions in a low-resolution image. The input to the second ANN are the curvature peaks computed for all pixels in each suspicious region. This comes from the fact that small tumors possess and identifiable signature in curvature-peak feature space, where curvature is the local curvature of the image data when viewed as a relief map. The output of this network is thresholded at a chosen level of significance to give a positive detection. Tests are performed using 60 radiographs taken from routine clinic with 90 real nodules and 288 simulated nodules. We employed free-response receiver operating characteristics method with the mean number of false positives (FP's) and the sensitivity as performance indexes to evaluate all the simulation results. The combination of the two networks provide results of 89%-96% sensitivity and 5-7 FP's/image, depending on the size of the nodules.