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
The purpose of this study was the development of a clustering methodology to deal with arterial pressure waveform (APW) parameters to be used in the cardiovascular risk assessment. One hundred sixteen subjects were monitored and divided into two groups. The first one (23 hypertensive subjects) was analyzed using APW and biochemical parameters, while the remaining 93 healthy subjects were only evaluated through APW parameters. The expectation maximization (EM) and k-means algorithms were used in the cluster analysis, and the risk scores (the Framingham Risk Score (FRS), the Systematic COronary Risk Evaluation (SCORE) project, the Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) and the PROspective Cardiovascular munster (PROCAM)), commonly used in clinical practice were selected to the cluster risk validation. The result from the clustering risk analysis showed a very significant correlation with ASSIGN (r = 0.582, p < 0.01) and a significant correlation with FRS (r = 0.458, p < 0.05). The results from the comparison of both groups also allowed to identify the cluster with higher cardiovascular risk in the healthy group. These results give new insights to explore this methodology in future scoring trials.

Abstract
A new approach is introduced for the automatic detection of the lumen axis of the common carotid artery in B-mode ultrasound images. The image is smoothed using a Gaussian filter and then a dynamic programming scheme extracts the dominant paths of local minima of the intensity and the dominant paths of local maxima of the gradient magnitude with the gradient pointing downwards. Since these paths are possible estimates of the lumen axis and the far wall of a blood vessel, respectively, they are grouped together into pairs. Then, a pattern of two features is computed from each pair of paths and used as input to a linear discriminant classifier in order to select the pair of paths that correspond to the common carotid artery. The estimated lumen axis is the path of local minima of the intensity that belongs to the selected pair of paths. The proposed method is suited to real time processing, no user interaction is required and the number of parameters is minimal and easy to determine. The validation was performed using two datasets, with a total of 199 images, and has shown a success rate of 99.5% (100% if only the carotid regions for which a ground truth is available are considered). The datasets have a large diversity of images, including cases of arteries with plaque and images with heavy noise, text or other graphical markings inside the artery region.

Abstract
A critical question in tapping behavior is to understand whether the temporal control is exerted on the duration and trajectory of the downward-upward hand movement or on the pause between hand movements. In the present study, we determined the duration of both the movement execution and pauses of monkeys performing a synchronization-continuation task (SCT), using the speed profile of their tapping behavior. We found a linear increase in the variance of pause-duration as a function of interval, while the variance of the motor implementation was relatively constant across intervals. In fact, 96% of the variability of the duration of a complete tapping cycle (pause + movement) was due to the variability of the pause duration. In addition, we performed a Bayesian model selection to determine the effect of interval duration (450 -1,000 ms), serial-order (1-6 produced intervals), task phase (sensory cued or internally driven), and marker modality (auditory or visual) on the duration of the movement-pause and tapping movement. The results showed that the most important parameter used to successfully perform the SCT was the control of the pause duration. We also found that the kinematics of the tapping movements was concordant with a stereotyped ballistic control of the hand pressing the push-button. The present findings support the idea that monkeys used an explicit timing strategy to perform the SCT, where a dedicated timing mechanism controlled the duration of the pauses of movement, while also triggered the execution of fixed movements across each interval of the rhythmic sequence.

Abstract
The retina is a unique region where the vascular condition can be observed in vivo. It is a window to the systemic vasculature, as the retinal blood vessels are exposed to the same stresses and pathologies as the whole vascular system. The retinal vasculature status can be evaluated with high-resolution eye fundus color images, and variations in the retinal microcirculation can be used as an indicator of a lesion in the cerebral microvasculature. The features measured on these images can characterize retinal arteriolar narrowing, which is a marker of hypertension, or diabetic retinopathy (DR).

Abstract
Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.