Sílvia Delgado Olabarriaga

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Segmentation of the object of interest is a difficult step in the analysis of digital images. Fully automatic methods sometimes fail, producing incorrect results and requiring the intervention of a human operator. This is often true in medical applications, where image segmentation is particularly difficult due to restrictions imposed by image acquisition,(More)
This note reports an experiment where a single Gaussian model and several Gaussian mixture models were used to model skin color in the rg chromaticity space. By using training and test databases containing millions of skin pixels, we show that mixture models can improve skin detection, but not always. There is a relevant operating region where no(More)
The quality of cardiac images acquired with multi-detector CT scanners has improved significantly, to the point where minimally invasive examination of the coronary arteries became reality. The interpretation of such images requires efficient post-processing tools to isolate the vessels from other structures, such that they can be properly analyzed(More)
We present an experimental setup to evaluate the relative peiformance of single gaussian and mixture of gaussians models for skin color modeling. Firstly, a sample set of J, J 20, 000 skin pixels from a number of ethnic groups is selected and represented in the chromaticity space. In the following , parameter estimation for both the single gaussian and(More)
This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method.(More)
Grid technology can offer a powerful infrastructure for a broad spectrum of (scientific) application areas, but the uptake of grids by " real " applications has been slow. Several aspects contribute to this scenario, among them a large gap between the communities that develop and use the technology. While grid developments focus mostly on function-ality,(More)
Functional Magnetic Resonance Imaging (fMRI) is a popular tool used in neuroscience research to study brain activation due to motor or cognitive stimulation. In fMRI studies, large amounts of data are acquired, processed, compared, annotated, shared by many users and archived for future reference. As such, fMRI studies have characteristics of applications(More)
This paper presents the design, implementation, and usage of a virtual laboratory for medical image analysis. It is fully based on the Dutch grid, which is part of the Enabling Grids for E-sciencE (EGEE) production infrastructure and driven by the gLite middleware. The adopted service-oriented architecture enables decoupling the user-friendly clients(More)
Image analysis is an important component of neuro-science research. The ICT infrastructure and technical knowledge needed to perform (large scale) neuroimaging studies, however, is often not available to the neuroscien-tists. The " Virtual Laboratory for e-Sciences " project provides an advanced (grid) infrastructure offering data and computing services to(More)
Computer-aided image analysis is becoming increasingly important to efficiently and safely handle large amounts of high-resolution images generated by advanced medical imaging devices. The development of medical image analysis (MIA) software with the required properties for clinical application, however, is difficult and labor-intensive. Such development(More)