Abstract
In a lung nodule detection task, parenchyma segmentation is crucial to obtain the region of interest containing all the nodules. Thus, the challenge is to devise a methodology that includes all the lung nodules, particularly those close to the walls, as the juxtapleural nodules. In this paper, different region growing approaches are proposed for the automatic segmentation of the lung parenchyma. The methodology is organized in five different steps: first, the image intensity is corrected to improve the contrast of the lungs. With that, the fat area is obtained, automatically deriving the interior of the lung region. Then, the traquea is extracted by a 3D region growing, being subtracted from the lung region results. The next step is the division of the two lungs to guarantee that both are separated. And finally, the lung contours are refined to provide appropriate final results. The methodology was tested in 50 images taken from the LIDC image database, with a large variability and, specially, including different types of lung nodules. In particular, this dataset contains 158 nodules, from which 40 are juxtapleural nodules. Experimental results demonstrate that the method provides accurate lung regions, specially including the centers of 36 of the juxtapleural nodules. For the other 4, although the centers are not included, parts of their areas are retained in the segmentation, which is useful for lung nodule detection.

Abstract
A method to detect juxta-pleural nodules with radius smaller than 5mm is presented. The intensity difference between nodules and parenchymal tissue as well as the nodules' natural roundness are exploited. Solid nodules are detected by selecting an appropriate threshold over a sliding window, whereas sub-solid/non-solid nodules are enhanced using multi-scale Laplacian-of-Gaussian filters. The 2D-wise outputs are combined to 3D, producing a final candidate list. False positive reduction is achieved with fixed rules and supervised learning. The achieved sensitivity is 57% with 4 false positives/scan, increasing to 62% if only solid nodules are considered. © 2017 SPIE.

Abstract
Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases. Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a down-sampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm. Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions. Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view.

Abstract
A complete analysis of the vascular system is a complex task since a large number of parameters are involved. In the research herein reported we present a novel medical framework called web-based integration for vascular expert research networks (Wivern) to be used in a multi-clinical department environment for the analysis of micro and macrocirculation. This tool can manage clinical information of several specialties, such as Neurology or Ophthalmology, and provides computer-aided tools to automatically analyze retinographies, carotid ultrasounds and blood pressure monitor signals, and to automatically compute cardiovascular risk stratification. Wivern is a web-based application with a user friendly interface that provides cross-platform compatibility and device independence. Several automated procedures are integrated within the framework, as a service on the web, to extract relevant parameters from clinical data, physiological signals and medical images. The application is planned for collecting and analyzing data in several clinical studies in different hospital centers to test their behavior and practical use of the different tools of the platform. The usefulness and validation of the system was achieved after the inclusion, by the different medical units, of 800 patients to analyze their hypertensive profile. Moreover, 800 retinal images were processed as well as 400 carotid were analyzed. Wivern provides a unique opportunity for vascular research since it enables an interdisciplinary and integrated study of the vascular network, allowing a more comprehensive evaluation of the consequences of any abnormality. The application also includes automated methods to process patient data in order to simplify the physician tasks.

Abstract
This paper presents an automatic approach for the estimation of Arteriolar-to-Venular Ratio (AVR) in retinal images. The method was assessed using the images of the INSPIRE-AVR database. A mean error of 0.05 was obtained when the method's results were compared with reference AVR values provided with this dataset, thus demonstrating the adequacy of the proposed solution for AVR estimation.

Abstract
This paper presents an automatic approach for artery/vein (A/V) classification based on the analysis of a graph representing the structure of the retinal vasculature. The entire vascular tree is classified by deciding on the type of each intersection point (graph node) and assigning one of two classes to each vessel segment (graph link). The final label for each vessel segment is accomplished by a combination of structural information taken from the graph (link class) with intensity features measured in the original color image. An accuracy of 88.0% was achieved for the 40 images of the INSPIRE-AVR dataset, thus demonstrating that our method outperforms state-of-the-art approaches for A/V classification.