Abstract
Life scientists often must count cells in microscopy images, which is a tedious and time-consuming task. Automatic approaches present a solution to this problem. Several procedures have been devised for this task, but the majority suffer from performance degradation in the case of cell overlap. In this article, we propose a method to determine the positions of macrophages and parasites in fluorescence images of Leishmania-infected macrophages. The proposed strategy is primarily based on blob detection, clustering, and separation using concave regions of the cells' contours. In comparison with the approaches of Nogueira (Master's thesis, Department of University of Porto Computer Science, 2011) and Leal et al. (Proceedings of the 9th international conference on Image Analysis and Recognition, Vol. II, ICIAR'12. Berlin, Heidelberg: Springer-Verlag; 2012. pp. 432-439), which also addressed this type of image, we conclude that the proposed methodology achieves better performance in the automatic annotation of Leishmania infections. (c) 2014 International Society for Advancement of Cytometry

Abstract

Abstract
Objective. Several classification systems have been launched to characterize Barrett's esophagus (BE) mucosa using magnification endoscopy with narrow band imaging (ME-NBI). The good accuracy and interobserver agreement described in the early reports were not reproduced subsequently. Recently, we reported somewhat higher accuracy of the classification developed by the Amsterdam group. The critical question then formulated was whether a structured learning program and the level of experience would affect the clinical usefulness of this classification. Material & methods: Two hundred and nine videos were prospectively captured from patients with BE using ME-NBI. From these, 70 were randomly selected and evaluated by six endoscopists with different levels of expertise, using a dedicated software application. First, an educational set was studied. Thereafter, the 70 test videos were evaluated. After classification of each video, the respective histological feedback was automatically given. Results. Within the learning process, there was a decrease in the time needed for evaluation and an increase in the certainty of prediction. The accuracy did not increase with the learning process. The sensitivity for detection of intestinal metaplasia ranged between 39% and 57%, and for neoplasia between 62% and 90%, irrespective of assessor's expertise. The kappa coefficient for the interobserver agreement ranged from 0.25 to 0.30 for intestinal metaplasia, and from 0.39 to 0.48 for neoplasia. Conclusion: Using a dedicated learning program, the ME-NBI Amsterdam classification system is suboptimal in terms of accuracy and inter- and intraobserver agreements. These results reiterate the questionable utility of corresponding classification system in clinical routine practice.

Abstract
In this paper, the topological and dynamical properties of the heart sounds are assessed. The signal is preprocessed and projected into an embedding subspace, which is more suitable to detect the irregularities and the unstable trajectories registered during the cardiac murmurs than the original heart sound signal. We present a method for heart murmur classification divided into five major steps: a) signal is divided into heart beats; b) entropy gradient envelogram is computed from the pre-processed signal; c) the orbital trajectories are reconstructed using the embedding theory; d) n orbits in the embedding subspace are extracted ( per heart beat); e) the median of the n orbits is used as an input to K-Nearest Neighbors ( KNN) classifier. The experimental results achieved are in agreement with the current state of art for heart murmur classification.

Abstract
In this paper we explore a novel feature for the segmentation of heart sounds: the entropy gradient. We are motivated by the fact that auscultations in real environments are highly contaminated by noise and results reinforce our suspicions that the entropy gradient is not only robust to such noise but maintains a high sensitivity to the S1 and S2 components of the signal. Our whole approach consists of three stages, out of which the last two are novel contributions to this field. The first stage consists of typical pre-processing and wavelet reconstruction to obtain the Shannon energy envelogram. On the second stage we use an embedding matrix to track the dynamics of the system, which is formed by delay vectors with higher dimension than the corresponding attractor. On the third stage, we use the eigenvalues and eigenvectors of the embedding matrix to estimate the entropy of the envelogram. Finite differences are used to estimate entropy gradients, in which standard peak picking approaches are used for heart sound segmentation. Experiments are performed on a public dataset of pediatric auscultations obtained in real environments and results show the promising potential of this novel feature for such noisy scenarios.

Abstract
Rheumatic heart disease is the serious consequence of repeated episodes of acute rheumatic fever. It is the major cause of heart valve damage resulting in morbidity and mortality. Its early detection is considered vital to control the disease's progression. The key manifestations that are visible in the early stages of this disease are changes in the thickness, shape and mobility of the mitral valve leaflets. Echocardiography based screening is sensitive enough to identify these changes in early stages of the disease. In this work, an automatic approach is proposed to measure, quantify and analyze the thickness of the anterior mitral leaflet, in an echocardiographic video. The shape of the anterior mitral leaflet is simplified via morphological skeletonization and spline modelling to get the central line of the leaflet. To analyze the overall thickness from the tip to its base, the anterior mitral leaflet is divided into four quartiles. In ach quartile the thickness is measured as the length of the line segment resulting from the intersection of the contour with the normal direction of the central point of each quartile. Finally, the thickness is analyzed by measuring the variance per quartile, divided by leaflet position (open, straight and closed). The comparison between the normal and pathological leaflets are also presented, exhibiting statistical significant differences in all quartiles, especially near the tip of the leaflet. © 2018 IEEE.