Abstract
Rheumatic heart disease is the result of damage to the heart valves, more often the mitral valve. The heart valves leaflets get inflamed, scarred and stretched which interrupts the normal blood flow, resulting into serious health condition. Measuring and quantifying clinically relevant features, like thickness, mobility and shape can help to analyze the functionality of the valve, identify early cases of disease and reduce the disease burden. To obtain these features, the first step is to automatically delineate the relevant structures, such as the anterior mitral valve leaflet, throughout the echocardiographic video. In this work, we proposed a near real time method to track the anterior mitral leaflet in ultrasound videos using the parasternal long axis view. The method is semi-automatic, requiring a manual delineation of the anterior mitral leaflet in the first frame of the video. The method uses mathematical morphological techniques to obtain the rough boundaries of the leaflet and are further refined by the localized active contour framework. The mobility of the leaflet was also obtained, providing us the base to analyze the functionality of the valve (opening and closing). The algorithm was tested on 67 videos with 6432 frames. It outperformed with respect to the time consumption (0.4 s/frame), with the extended modified Hausdorff distance error of 3.7 pixels and the improved tracking performance (less failure).

Abstract
Delimitation and classification of each heart sound is a rather difficult task. Elevated heart rates, as found in pediatrics and in some adults as well, influence some of the most reliable features used by existing methods. Furthermore, in real life scenarios, cardiologists will not have the time to acquire the signal's length required by some of the existing algorithms, which make us think that different approaches ought to be pursued. This paper presents the work on heart sound segmentation using structural and energy based features. It is an attempt to not rely on features considered crucial to most existing approaches. Yet, it achieves a high sensitivity and specificity comparable to some literature.

Abstract
Echocardiography is an important tool to detect early evidence of mitral valve degradation associated with rheumatic heart disease. The segmentation and tracking of the Anterior Mitral Leaflet helps to quantify the morphologic valve anomalies, such as the leaflet thickening, shape and the mobility changes. The tracking of this leaflet throughout the cardiac cycle is still an open challenge in the research community. The widely used active contours segmentation framework fails when faced with large leaflet displacement. In this work, we propose the integration of optical flow in an open-ended active contour framework to address this difficulty. This additional information promotes solutions with contours next to high leaflet displacements, resulting in superior performance. The algorithm was tested on 9 fully annotated real clinical videos, acquired from the parasternal long axis view. The algorithm is compared with our previous work. Results show a clear improvement in situations where the leaflet exhibits large displacement or irregular shapes, with an average error of 4.5 pixels and a standard deviation of 2 pixels.

Abstract
The analysis of heart sounds is a challenging task, due to the quick temporal onset between successive events and the fact that an important fraction of the information carried by phonocardiogram (PCG) signals lies in the inaudible part of the human spectrum. For these reasons, computer-aided analysis of the PCG can dramatically improve the quantity of information recovered from such signals. In this paper, a hidden semi-Markov model (HSMM) is used to automatically segment PCG signals. In the proposed models, the emission probability distributions are approximated via Gaussian mixture model (GMM) priors. The choice of GMM emission probability distributions allow to apply re-estimation routines to automatically adjust the HSMM emission probability distributions to each subject. Building on the proposed method for fine tuning emission distributions, a novel subject-driven unsupervised heart sound segmentation algorithm is proposed and validated over the publicly available PhysioNet dataset. Perhaps surprisingly, the proposed unsupervised method achieved results in line with state-of-the-art supervised approaches, when applied to long heart sounds.

Abstract
This work presents a method for the automatic segmentation of metacarpus and phalange bones in ultrasound images of the second metacarpophalangeal joint (MCPJ) using Active Contours. The MCPJ is known to be the one of the first structures to be affected by rheumatic diseases like rheumatoid arthritis. The early detection and follow-up of this disease is important to prevent irreversible damage of the joints, which occurs continuously and faster if no treatment is used. To our knowledge, there is no automatic system to quantify the extension of the lesions resulting from rheumatic activity. The objective of this work is to identify the metacarpus and the phalange bones using local active contours. To our knowledge, there is no well established method for this problem and this technique has not been used yet in these structures. Results proved that the automatic segmentation is possible with an error of 3 pixels for a confidence of 80%.

Abstract
Segmentation is a vital step for pattern recognition systems used in in-body imaging scenarios. In this paper we compare the performance of three popular segmentation algorithms (mean shift, normalized cuts, level-sets) when applied to two distinct in-body imaging scenarios: chromoendoscopy and narrow-band imaging. Observation shows that the model-based algorithm did not perform well, when compared to its segmentation by clustering alternatives. Normalized cuts obtained the best performance although future work hints that texture similarity should be further explored in order to increase segmentation performance in this type of scenarios.