Catálogo de publicaciones - libros

Digital Rights Management (DRM) systems’ interoperability is becoming one of the main obstacles for their wider adoption, especially from medium and small size users. Interoperability issues affect, among others, the management of content usage rules by third parties (authorities) and the automation of licensing procedures upon the purchase of digital content. The fundamental question of who is handling content licenses in the national or global DRM value chain is complex, with business, social and technological extensions. In this paper, we discuss current trends in DRM systems technology and business modelling and briefly present a proposal for handling digital content licensing, Distributed License Catalogues (DLCs). The DLC concept, borrowed from web engineering, makes available (“advertises”) content or services concerning DRM functionalities, enabling multi-party DRM eco-systems.

AXMEDIS project (Automating Production of Cross Media Content for Multi-channel Distribution) is partially funded by the European Commission to create an innovative technology framework for the automatic production, protection and distribution of digital cross-media contents over a range of different media channels including PC (on the internet), PDA, kiosk, mobile phones and i-TV (interactive-TV). The AXMEDIS project has proposed a set of integrated solutions and technologies that covers data model and DRM. This paper presents a brief introduction to the AXMEDIS IST FP6 EC project, while discussing the new functionalities enabled by the AXMEDIS architecture and solution in terms of interoperable content and DRM among different distribution channels. For further details on the AXMEDIS project, see the project website at .

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three -nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes.

Computer-aided classification of benign and malignant endometrial tissue, as depicted in 2D gray scale transvaginal ultrasonography (TVS), was attempted by computing texture-based features. 65 TVS endometrial images were collected (15 malignant, 50 benign) and processed with a wavelet based enhancement technique. Two regions of interest (ROIs) were identified (endometrium, endometrium margin) on each processed image. Thirty-two textural features were extracted from each ROI employing first and second order statistics texture analysis algorithms. Textural feature-based models were generated for differentiating benign from malignant endometrial tissue employing stepwise logistic regression analysis. Models’ performance was evaluated by means of receiver operating characteristics (ROC) analysis. The best benign versus malignant classification was obtained from the model combining three textural features from endometrium and four textural features from endometrium margin, with corresponding area under ROC curve (Az) 0.956.

Automated detection of masses on mammograms is challenged by the presence of dense breast parenchyma. The aim of this study was to investigate the feasibility of using wavelet-based feature analysis for differentiating masses, of varying sizes, from normal dense tissue on mammograms. The dataset analyzed consists of 166 regions of interest (ROIs) containing spiculated masses (60), circumscribed masses (40) and normal dense tissue (66). A set of ten multiscale features, based on intensity, texture and edge variations, were extracted from the ROIs subimages provided by the overcomplete wavelet transform. Logistic regression analysis was employed to determine the optimal multiscale features for differentiating masses from normal dense tissue. The classification accuracy in differentiating circumscribed masses from normal dense tissue is comparable with the corresponding accuracy in differentiating spiculated masses from normal dense tissue, achieving areas under the ROC curve 0.895 and 0.875, respectively.

In presence of dense mammographic parenchyma, microcalcifications (MCs) are obscured by anatomical structures, resulting in missed or/and false detections. Image analysis methods applied to improve visualization, detection and/or characterization of MCs, are targeted to MC SNR improvement and are unavoidably accompanied by MC background over-enhancement or false positive (FP) detections. A set of new features is proposed, extracted statistically with Principal Component Analysis from the wavelet coefficients of real subtle MCs in dense parenchyma. Candidate MCs are segmented and classified with the proposed features, using Linear Discriminant Analysis. Our method achieved 69% true positive fraction of MC clusters with 0.2 FPs per image in a dataset with 54 subtle MC clusters in extremely dense parenchyma.

The aim of this study was to investigate the usefulness of gray scale morphological analysis in the assessment of atherosclerotic carotid plagues. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques (Stroke, Transient Ischaemic Attack -TLA, Amaurosis Fugax-AF). The morphological pattern spectra of gray scale images were computed and two different classifiers named the Probabilistic Neural Network (PNN) and the Support Vector Machine (SVM) were evaluated for classifying these spectra into two classes: asymptomatic or symptomatic. The highest percentage of correct classifications score was 66,8% and was achieved using the SVM classifier. This score is slightly lower than texture analysis carried out on the same data set.