Abstract
This paper proposes a new approach for the automatic segmentation of the carotid adventitia in longitudinal B-scans, with and without the presence of plaque. The top and bottom adventitia contours are jointly detected with a 3D dynamic programing scheme that searches for the best pair of boundaries according to a specified fuzzy cost function. Some discriminating features of the adventitia in B-mode images are used to reduce the attraction by other edges. The final contours are filtered with a smoothing spline fitting. The proposed approach was quantitatively evaluated in a set of 38 images. In order to avoid high correlation of the results, a maximum of two images was selected from each patient. The carotid boundaries manually traced by a medical expert were used as the ground truth. Several statistics show that the proposed algorithm gives good results in most of the cases, including many poor quality images. Examples of the detected contours are presented and compared with the ground truth.

Abstract
A new algorithm for an effective and automatic segmentation of the carotid wall in ultrasonic images is proposed. It combines the speed of thresholding algorithms with the accuracy, flexibility and robustness of a successful geometric active contour model which incorporates an optimal image segmentation model in a level set framework. Due to the multiphase nature of these images, a sequential minimum cross entropy thresholding is used to get a first approximation of the segments, reducing the problem to a two phase segmentation. This thresholding solution is then used as a starting point for a two phase piecewise constant version of a geometric active contour model to reduce noise, smooth contours, improve their position accuracy and close eventual gaps in the carotid wall.

Abstract
The purpose of the research herein presented is the automatic detection of lung boundaries in posterior-anterior digital chest radiographs. The precise location of the two lungs is important in a computer-aided diagnosis system as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by an experienced radiologist. For this particular purpose, two programs with friendly interfaces were developed. The achieved comparison results demonstrate the good performance of the automatic method.

Abstract

Abstract
With the increase of Endoscopic Capsule (EC) exams, the amount of video data to be analyzed by automated image processing algorithms grows exponentially and standard desktop computers start to present limitations to complete that procedure in an acceptable "clinical time". This has motivated us to assess the potential of Grid computing paradigm to process and analyze EC data, using existing Grid infrastructures. We used our Automated Topografic Segmentation (ATS) algorithm as a case study to run experiments on the Ibergrid Grid infrastructure. As a result, we were able to port the existing ATS algorithm used in routine to run on the Grid seamlessly. A friendly portal environment was adopted, allowing for clinical end-users to harness from existing production Grids in a practical way. The processing time could be reduced in some cases but, when considering job processing overheads, the desktop version is preferable for small scale processing. These results show that Grid computing is a promising technology to process massive amounts of EC data, but current infrastructures present a high variability in jobs processing times, especially when compared with dedicated clusters.

Abstract
We describe a real-time facade tracking system that uses, as setup information, only two images of a facade, captured on the moment. No more previous information is needed, such as a facade 3D model, dimensions or aspect ratio. Feature points and their local descriptors are extracted from that pair of images and used during the detection and tracking of the facade. Additionally, parallax and topological information is also used in order to increase the overall robustness of the tracking process. Experiments show that the system can detect and track a wide variety of facades, including those that are not entirely planar, partially occluded or have few distinguishable visual landmarks. The reliance on on-the-spot information, alone, makes this system useful for Outdoor Augmented Reality applications, in an Anywhere Augmentation urban context.