Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model.

Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy patients with different degrees of diabetic retinopathy (without macular edema, with macular edema and with lesions in the photoreceptor layers). Quantitative results are in line with the state of the art of this domain, providing accurate segmentations of the retinal layers even when significative pathological alterations are present in the eye fundus. Therefore, the proposed method is robust enough to be used in complex environments, making it feasible for the ophthalmologists in their routine clinical practice.

Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment.

BACKGROUND AND OBJECTIVE: Cardiovascular (CV) risk stratification is a highly complex process involving an extensive set of clinical trials to support the clinical decision-making process. There are many clinical conditions (e.g. diabetes, obesity, stress, etc.) that can lead to the early diagnosis or establishment of cardiovascular disease. In order to determine all these clinical conditions, a complete set of clinical patient analyses is typically performed, including a physical examination, blood analysis, electrocardiogram, blood pressure (BP) analysis, etc. This article presents a web-based system, called Hydra, which integrates a full and detailed set of services and functionalities for clinical decision support in order to help and improve the work of clinicians in cardiovascular patient diagnosis, risk assessment, treatment and monitoring over time. METHODS: Hydra integrates a number of different services: a service for inputting all the information gathered by specialists (physical examination, habits, BP, blood analysis, electrocardiogram, etc.); a tool to automatically determine the CV risk stratification, including well-known standard risk stratification tables; and, finally, various tools to incorporate, analyze and graphically present the records of the ambulatory BP monitoring that provides BP analysis over a given period of time (24 or 48 hours). In addition, the platform presents a set of reports derived from all the information gathered from the patient in order to support physicians in their clinical decisions. RESULTS: Hydra was tested and validated in a real domain. In particular, internal medicine specialists at the Hypertension Unit of the Santiago de Compostela University Hospital (CHUS) validated the platform and used it in different clinical studies to demonstrate its utility. It was observed that the platform increased productivity and accuracy in the assessment of patient data yielding a cost reduction in clinical practice. CONCLUSIONS: This paper proposes a complete platform that includes different services for cardiovascular clinical decision support. It was also run as a web-based application to facilitate its use by clinicians, who can access the platform from any remote computer with Internet access. Hydra also includes different automated methods to facilitate the physicians' work and avoid potential errors in the analysis of patient data.

Computer aided diagnosis system for retinal analysis: automatic assessment of the vascular tortuosity.

The tortuosity of a vessel, that is, how many times a vessel curves, and how these turns are, is an important value for the diagnosis of certain diseases. Clinicians analyze fundus images manually in order to estimate it, but there is many drawbacks as it is a tedious, time-consuming and subjective work. Thus, automatic image processing methods become a necessity, as they make possible the efficient computation of objective parameters. In this paper we will discuss Sirius (System for the Integration of Retinal Images Understanding Service), a web-based application that enables the storage and treatment of various types of diagnostic tests and, more specifically, its tortuosity calculation module.

Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards.

Dry eye syndrome is affecting a remarkable percentage of population. The prevalence is 10-15% of normal population, and 18-30% of contact lenses users. The break-up time (BUT) is a clinical test used for the diagnosis of this disease. In this work, we perform an analysis of parameters for a global and a local automatic computation of the BUT measure, based on criteria of specificity and sensitivity. We have tested our methodology on a dataset composed of 18 videos annotated by 4 different experts. The local analysis preserves the results of the global approach providing useful additional information about the break-up tear zone.

Reliable monitoring system for arteriovenous ratio computation.

The degree of narrowing or widening in retinal vessels related to several cardiovascular diseases such as hypertension or diabetes may be measured by the arteriovenous ratio (AVR), that is, the relation between the artery and vein retinal vessel widths. Nevertheless, its lack of reproducibility, due mainly to a laborious manual calculation and the dependence of the vessels selected for its estimation, hinders its use in daily medical practice. This variation makes difficult to monitor the patient's condition over time. This paper describes a reliable AVR monitoring system which computes automatically the AVR from several images of the same patient acquired at different times using the same vessels measured at the same points. The system has been evaluated in a large data set of 158 pairs of images and good correlation results between medical experts and the system have been achieved.

Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification.

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%.

Algorithm for registration of full Scanning Laser Ophthalmoscope video sequences.

Fluorescein angiography is an established technique for examining the functional integrity of the retinal microcirculation for early detection of changes due to retinopathy. This paper describes a new method for the registration of large Scanning Laser Ophthalmoscope sequences (SLO), where the patient has been injected with a fluorescent dye. This allows the measurement of parameters such as the arteriovenous passage time. Due to the long time needed to acquire these sequences, there will inevitably be eye movement, which must be corrected prior to the application of quantitative analysis. The algorithm described here combines mutual information-based registration and landmark-based registration. The former will allow the alignment of the darkest frames of the sequence, where the dye has not still arrived to the retina, because of its ability to work with images without a preprocessing or segmentation, while the latter uses relevant features (the vessels) extracted by means of a robust creaseness operator, to get a very fast and accurate registration. The algorithm only detects rigid transformations but proves to be robust against the slight alterations derived from the eye location perspective during acquisition. Results were validated by expert clinicians.

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.

Computer-aided diagnoses: automatic detection of lung nodules.

This work describes a computational scheme for automatic detection of suspected lung nodules in a chest radiograph. A knowledge-based system extracts the lung masks over which we will apply the nodule detection process. First we obtain the normalized cross-correlation image. Next we detect suspicious regions by assuming a threshold. We examine the suspicious regions using a variable threshold which results in the growth of the suspicious areas and an increase in false positives. We reduce the large number of false positives by applying the facet model to the suspicious regions of the image. An algorithmic classification process gives a confidence factor that a suspicious region is a nodule. Five chest images containing 30 known nodules were used as a training set. We evaluated the system by analyzing 30 chest images with 40 confirmed nodules of varying contrast and size located in various parts of the lungs. The system detected 100% of the nodules with a mean of six false positives per image. The accuracy and specificity were 96%.

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection.

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.