Abstract
The endoscopic capsule is a recent technological breakthrough with high clinical importance. Exam analysis duration is its main setback, requiring an average of two hours from a trained specialist. Automation is required and this paper presents a topographic segmentation tool using low-level features that can reduce annotation times up to 15 minutes per exam. This is accomplished using Bayesian classifiers and MPEG-7 visual descriptors.

Abstract
The goal of the Vital Responder research project is to explore the synergies between innovative wearable technologies, scattered sensor networks, intelligent building technology and precise localization services to provide secure, reliable and effective first-response systems in critical emergency scenarios. Critical events, such as natural disaster or other large-scale emergency, induce fatigue and stress in first responders, such as fire fighters, policemen and paramedics. There are distinct fatigue and stress factors (and even pathologies) that were identified among these professionals. Nevertheless, previous work has uncovered a lack of real-time monitoring and decision technologies that can lead to in-depth understanding of the physiological stress processes and to the development of adequate response mechanisms. Our "silver bullet" to address these challenges is a suite of non-intrusive wearable technologies, as inconspicuous as a t-shirt, capable of gathering relevant information about the individual and disseminating this information through a wireless sensor network. In this paper we will describe the objectives, activities and results of the first two years of the Vital Responder project, depicting how it is possible to address wearable sensing challenges even in very uncontrolled environments. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.

Abstract
Digital stethoscopes have been drawing the attention of the biomedical engineering community for some time now, as seen from patent applications and scientific publications. In the future, we expect'intelligent stethoscopes' to assist the clinician in cardiac exam analysis and diagnostic, potentiating functionalities such as the teaching of auscultation, telemedicine, and personalized healthcare. In this paper we review the most recent heart sound processing publications, discussing their adequacy for implementation in digital stethoscopes. Our results show a body of interesting and promising work, although we identify three important limitations of this research field: lack of a set of universally accepted heart-sound features, badly described experimental methodologies and absence of a clinical validation step. Correcting these flaws is vital for creating convincing next-generation'intelligent' digital stethoscopes that the medical community can use and trust.

Abstract
Statistical pattern recognition research, namely in applied computer vision, typically needs highly accurate massive datasets to train and test its classifiers. This paper presents extensive work for creating a large clinically annotated dataset of high confidence events for gastroenterology. More specifically, we address images and videos obtained using endoscopic capsule imaging technology, which contain some kind of lesion. The purpose of such dataset is to boost scientific research in computer aided diagnostic systems for a technology that would clearly benefit from them.

Abstract
Capsule endoscopy is a recent technology with a clear need for automatic tools that reduce the long exam annotation times of exams. We have previously developed a topographic segmentation method, which is now improved by using spatial and temporal position information. Two approaches are studied: using this information as a confidence measure for our previous segmentation method, and direct integrating of this data into the image classification process. These allow us not only to automatically know when we have obtained results with error magnitudes close to human errors, but also to reduce these automatic errors to much lower values. All the developed methods have been integrated in the CapView annotation software, currently used for clinical practice in hospitals responsible for over 250 capsule exams per year, and where we estimate that the two hour annotation times are reduced by around 15 minutes.