Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

>Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and media-adventitia boundaries has been developed. This comprises two different steps for contour initialization, one for each corresponding contour of interest, based on the results of texture analysis, and a procedure for approximating the initialization results with smooth continuous curves. A multilevel Discrete Wavelet Frames decomposition is used for texture analysis, whereas Radial Basis Function approximation is employed for producing smooth contours. The proposed method shows promising results compared to a previous approach for texture-based IVUS image analysis

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery

This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile consists of two separate models, that is, the long-term interests are stored in a skeleton profile on the server and the short-term interests in a detailed profile in the handset. The user profile enables a high-level filtering of available news content on the server, followed by matching of detailed user preferences in the handset. The highest rated items are recommended to the user, by employing an efficient ranking process. The paper focuses on a two-level learning process, which is employed on the client side in order to automatically update both user profile models. It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. The system's learning performance has been systematically evaluated based on data collected from regular system users

† Olympic Games Organizing Committee Athens 2004

This paper presents a novel approach for generating VRML animation sequences from Sign Language notation, based on MPEG-4 Body Animation. Sign Language notation, in the well-known SignWriting system, is provided as input and is initially converted to SWML (SignWriting Markup Language), an XML-based format which has recently been developed for the storage, indexing and processing of SignWriting notation. Each sign box (basic sign) is then converted to a sequence of Body Animation Parameters (BAPs) of the MPEG-4 standard, corresponding to the represented gesture. These sequences, which can also be coded and/or reproduced by MPEG-4 BAP players, are then used to animate H-anim compliant VRML avatars, reproducing the exact gestures represented in sign language notation. Envisaged applications include producing signing avatars for interactive information systems (Web, E-mail, info– kiosks) and TV newscasts for persons with hearing disabilities. 1