Using human rights for sexual and reproductive health: improving legal and regulatory frameworks

This paper describes the development of a tool that uses human rights concepts and methods to improve relevant laws, regulations and policies related to sexual and reproductive health. This tool aims to improve awareness and understanding of States' human rights obligations. It includes a method for systematically examining the status of vulnerable groups, involving non-health sectors, fostering a genuine process of civil society participation and developing recommendations to address regulatory and policy barriers to sexual and reproductive health with a clear assignment of responsibility. Strong leadership from the ministry of health, with support from the World Health Organization or other international partners, and the serious engagement of all involved in this process can strengthen the links between human rights and sexual and reproductive health, and contribute to national achievement of the highest attainable standard of health.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.