CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.

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.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A Faceted Classification Scheme for Computer-Mediated Discourse

This article describes a classification scheme for computer-mediated discourse that classifies samples in terms of clusters of features, or “facets”. The goal of the scheme is to synthesize and articulate aspects of technical and social context that influence discourse usage in CMC environments. The classification scheme is motivated, presented in detail with support from existing literature, and illustrated through a comparison of two types of weblog (blog) data. In concluding, the advantages and limitations of the scheme are weighed.

Articulated Narrowcasting for Privacy and Awareness in Multimedia Conferencing Systems and Design for Implementation Within a SIP Framework

This article proposes a new focus of research for multimedia conferencing systems which allows a participant to flexibly select another participant or a group for media transmission. For example, in a traditional conference system, participants voices might by default be shared with all others, but one might want to select a subset of the conference members to send his/her media to or receive media from. We review the concept of narrowcasting, a model for limiting such information streams in a multimedia conference, and describe a design to use existing standard protocols (SIP and SDP) for controlling fine-grained narrowcasting sessions.

Development of Fonts for African Scripts: Using Computer Technologies to Preserve Africa s Written Heritage

The issue of the font making for modern indigenous African scripts is briefly discussed. Encoding problems, including the Unicode standard, are addressed. Font samples are presented for some of the scripts.