Abstract
Background and aim: Some studies suggest that narrow-band imaging (NBI) can be more accurate at diagnosing gastric intestinal metaplasia and dysplasia than white-light endoscopy (WLE) alone. We aimed to assess the real-time diagnostic validity of high resolution endoscopy with and without NBI in the diagnosis of gastric premalignant conditions and to derive a classification for endoscopic grading of gastric intestinal metaplasia (EGGIM). Methods: A multicenter prospective study (five centers: Portugal, Italy, Romania, UK, USA) was performed involving the systematic use of high resolution gastroscopes with image registry with and without NBI in a centralized informatics platform (available online). All users used the same NBI classification. Histologic result was considered the diagnostic gold standard. Results: A total of 238 patients and 1123 endoscopic biopsies were included. NBI globally increased diagnostic accuracy by 11 percentage points (NBI 94% vs. WLE 83%; P<0.001) with no difference in the identification of Helicobacter pylori gastritis (73% vs. 74%). NBI increased sensitivity for the diagnosis of intestinal metaplasia significantly (87% vs. 53%; P<0.001) and for the diagnosis of dysplasia (92% vs. 74%). The added benefit of NBI in terms of diagnostic accuracy was greater in OLGIM III/IV than in OLGIM I/II (25 percentage points vs. 15 percentage points, respectively; P<0.001). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for EGGIM in the identification of extensive metaplasia was 0.98. Conclusions: In a real-time scenario, NBI demonstrates a high concordance with gastric histology, superior to WLE. Diagnostic accuracy higher than 90% suggests that routine use of NBI allows targeted instead of random biopsy samples. EGGIM also permits immediate grading of intestinal metaplasia without biopsies and merits further investigation.

Abstract
This work presents a new approach for the identification of the joint capsule in ultrasound images of the metacarpophalangeal joint. These images are used to diagnose rheumatic diseases which are one of the main causes of impairment and pain in developed countries. The early diagnosis of these conditions is crucial to a proper treatment and follow-up and so, this work contributes to the automatic extraction of relevant information from the resulting images. The algorithm uses the metacarpus, phalange and extensor tendon positions to create a region of interest. Next, a split and merge approach is used to identify the joint capsule, where the split is done using the Simple Linear Iterative Clustering algorithm and the merge is achieved with a special region growing with shape constraints. After that, the results are refined using a Localizing Active Contours method. Results shown that the segmentation is possible with 60% of the joint capsules identified with a Dice Coefficient higher than 0.7. © 2018 IEEE.

Abstract
Local descriptors coupled with robust methods for learning visual dictionaries have been a pivotal tool in computer vision. Although the identification of similar patterns is commonly conducted on some stage of the bag-of-words framework, a prior assessment of spatial local similarities can be indicative of specific objects, and thus improved recognition rates. In this work we delve a function of similarity for enhancing the discriminative power of local constrained SIFT descriptors. Motivated by gastrointestinal images where diagnosis through endoscopy plays a decisive role in cancer detection and resulting prognosis, visual cues in these early stages are slim and of difficult perception. In order to capture these patterns we propose a self-similarity approach (based on a neighbourhood analysis of SIFT descriptors) to assess local variances through a weight function. Based on extensive simulations our approach achieved a performance of 88%: 3% higher than the standard SIFT, 10% higher than Haar wavelet and 13% higher than LBPs.

Abstract
Acoustic heart signals are generated by a turbulence effect created when the heart valves snap shut, and therefore carrying significant information of the underlying functionality of the cardiovascular system. In this paper, we present a method for heart murmur classification divided into three major steps: a) features are extracted from the heart sound; b) features are selected using a Backward Feature Selection algorithm; c) signals are classified using a K-nearest neighbor's classifier. A new set of fractal features are proposed, which are based on the distinct signatures of complexity and self-similarity registered on the normal and pathogenic cases. The experimental results show that fractal features are the most capable of describing the non-linear structure and the underlying dynamics of heart sounds among the all feature families tested. The classification results achieved for the mitral auscultation spot (88% of accuracy) are in agreement with the current state of the art methods for heart murmur classification.

Abstract
This work proposes a new approach for the segmentation of the extensor tendon in ultrasound images of the second metacarpophalangeal joint (MCPJ). The MCPJ is known to be frequently involved in early stages of rheumatic diseases like rheumatoid arthritis. The early detection and follow up of these diseases is important to start and adapt the treatments properly and, in that way, preventing irreversible damage of the joints. This work relies on an active contours framework, preceded by a phase symmetry preprocessing and with prior knowledge energies, to automatically identify the extensor tendon. Active contours methods are widely used in ultrasound images because of their robustness to speckle noise and ability to join unconnected smaller regions into a coherent shape. The tendon is formulated as a line so open ended active contours were used. Phase symmetry highlights the tendon, by setting a proper scale range and angle span. The distance between structures and the tendon slope were also included to enforce the model based on anatomical characteristics. And finally, the concavity measures were used because, given the anatomy of the finger, we know that the tendon line should have less than two concavities. To solve the active contours energy minimization a genetic algorithm approach was used. Several energy metric configurations were compared using the modified Hausdorff distance and results showed that this segmentation is not only possible, but exhibits errors smaller than 0.5 mm with a confidence of 95% with the phase symmetry preprocessing and energies based on the line neighborhood, area ratio, slope, and concavity measurements.

Abstract
The gastroenterology specialty could benefit from the introduction of Computer Assisted Decision (CAD) systems, since gastric cancer is a serious concern in which an accurate and early diagnosis usually leads to a good prognosis. Still, the way doctors interact with these systems is very important because it will often determine its embracement or rejection, as any gains in productivity will frequently hinge on how comfortable they are with it. Using other types of interaction paradigms such as voice and motion control, is important in a way that typical inputs such as keyboard and mouse are sometimes not the best choice for certain clinical scenarios. In order to ascertain how a doctor could control a hypothetical CAD system during a gastroenterology exam, we measured the natural response of users when faced with three different task requests, using three types of interaction paradigms: voice, gesture and endoscope. Results fit in what was expected, with gesture control being the most intuitive to use, and the endoscope being on the other edge. All the technologies are mature enough to cope with the response concepts the participants gave us. However, when having into account the scenario context, better natural response scores may not always be the best choice for implementation. That way, simplification or reduction of tasks, along with a well tought-out interface, or even mixing more oriented paradigms for particular requests, could allow for better system control with fewer inconveniences for the user.