Miguel Coimbra

Abstract
This paper proposes a computer assisted diagnostic system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim at validating these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH2 and ISIC dermoscopy datasets. An extensive experimental analysis reveals two important findings: 1) the proposed segmentation method mimics the ground truth data; and 2) the most significant melanoma characteristics in the lesion actually lie on the lesion periphery.

Abstract

Abstract
Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.

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