Abstract
The endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper shows how support vector machines can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal transit times. According to medical specialists, this can reduce exam annotation times by up to 12%.

Abstract
The DigiScope project aims at developing a digitally enhanced stethoscope capable of using state of the art technology in order to help physicians in their daily medical routine. One of the main tasks of DigiScope is to build a repository of auscultations (sound and medical related data). In this work, we present a preliminary analysis and study of the first auscultations performed on children of a Brazilian hospital. Results indicate that classifiers can be obtained that distinguish reasonably well patients with cardiac pathologies from those that do not have pathologies.

Abstract
MPEG-2 compressed domain information, namely motion vectors and DCT coefficients, is filtered and manipulated to obtain a motion field using a two-dimensional (2-D) translational model. The results are compared to a popular optical flow method, more specifically the one presented by Lucas and Kanade, revealing very good results. Our method provides a very fast motion estimation tool that can be useful for applications where algorithmic cost is critical, such as surveillance systems. All methods are theoretically explained and their efficiency confirmed on real-world data.

Abstract
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MITBIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.

Abstract
Several image classification problems are handled using a classical statistical pattern recognition methodology: image segmentation, visual feature extraction, classification. The accuracy of the solution is typically measured by comparing automatic results with manual classification ones, where the distinction between these three steps is not clear at all. In this paper we will focus on one of these steps by addressing the following question: does the visual relevance exploited by segmentation algorithms reflect the semantic relevance of the manual annotation performed by the user? For this purpose we chose a gastroenterology scenario where clinicians classified a set of images into three different types (cancer, pre-cancer, normal), and manually segmented the area they believe was responsible for this classification. Afterwards, we have quantified the performance of two popular segmentation algorithms (mean shift, normalized cuts) on how well they produced one image patch that approximates manual annotation. Results showed that, for this case study, this resemblance is quite close for a large percentage of the images when using normalized cuts.

Abstract
Endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper proposes and compares two approaches (Bayesian and support vector machines) that can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal sections and corresponding transit times. According to medical specialists, this can reduce exam annotation times by up to 12% (15 min). This automatic tool has been integrated into our CapView annotation software that is currently being used by three medical institutions.