Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle.

BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve. METHODS: This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles. RESULTS: The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated. CONCLUSIONS: The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

Energy efficient simulator for patient monitoring in Body Sensor Networks.

The medical tracking of patients carried out from a distance by means of wireless technology, incorporating various sensors simultaneously connected to the body of an individual, can promote health in suitable time. This article proposes a simulator for the monitoring of human health. This prototype simulates the workings of a BSN (Body Sensor Network) functioning as a sensor (electrophysiological signal) that is to be monitored, which utilizes a strategy based on the concept of data fusion. This tool incorporates a policy of economizing energy which has as its main objective increasing the operating time of the system.

A new model for programming software in body sensor networks.

A Body Sensor Network (BSN) must be designed to work autonomously. On the other hand, BSNs need mechanisms that allow changes in their behavior in order to become a clinically useful tool. The purpose of this paper is to present a new programming model that will be useful for programming BSN sensor nodes. This model is based on an intelligent intermediate-level compiler. The main purpose of the proposed compiler is to increase the efficiency in system use, and to increase the lifetime of the application, considering its requirements, hardware possibilities and specialist knowledge. With this model, it is possible to maintain the autonomous operation capability of the BSN and still offer tools that allow users with little grasp on programming techniques to program these systems.

Classification and retrieval of medical images in an integrated healthcare environment.

This work presents a new approach for classification and retrieval of echocardiographic images from textual information of the anatomical structures and diagnosis features. These textual attributes will be acquired from the electronic medical report generated in an integrated healthcare environment. The medical report is provided by a specialist in the area during the analysis of the medical image stored in a PACS environment. Such innovation guarantees a more accurate classifier and a better optimization of the medical work, since the medical report and the attributes for the medical image classifier will be created at the same time. The system is being developed in the University Hospital of the University of Brasilia.

Application-oriented programming model for sensor networks embedded in the human body.

This work presents a new programming model for sensor networks embedded in the human body which is based on the concept of multi-programming application-oriented software. This model was conceived with a top-down approach of four layers and its main goal is to allow the healthcare professionals to program and to reconfigure the network locally or by the Internet. In order to evaluate this hypothesis, a benchmarking was executed in order to allow the assessment of the mean time spent in the programming of a multi-functional sensor node used for the measurement and transmission of the electrocardiogram.