Visual Communications Technologies Center

Donostia / San Sebastián, Spain

Visual Communications Technologies Center

Donostia / San Sebastián, Spain

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Macia I.,Visual Communications Technologies Center | Grana M.,University of the Basque Country | Paloc C.,Visual Communications Technologies Center
Knowledge and Information Systems | Year: 2012

We have detected the lack of a widely accepted knowledge representation model in the area of Blood Vessel analysis. We find that such a tool is needed for the future development of the field and our own research efforts. It will allow easy reuse of software pieces through appropriate abstractions, facilitating the development of innovative methods, procedures and applications. We include a thorough review of vascular morphology image analysis. After the identification of the key representation elements and operations, we propose a Vessel Knowledge Representation (VKR) model that would fill this gap. We give insights into its implementation based on standard Object-Oriented Programming tools and paradigms. The VKR would easily integrate with existing medical imaging and visualization software platforms, such as the Insight ToolKit (ITK) and Visualization Toolkit (VTK). © 2011 Springer-Verlag London Limited.


Macia I.,Visual Communications Technologies Center | Macia I.,University of the Basque Country | Grana M.,University of the Basque Country | Paloc C.,Visual Communications Technologies Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

We propose the development of a knowledge representation model in the area of Blood Vessel analysis, whose need we feel for the future development of the field and for our own research efforts. It will allow easy reuse of software pieces through appropriate abstractions, facilitating the development of innovative methods, procedures and applications. In this paper we present some key ideas that will be fully developed elsewhere. © Springer-Verlag 2010.


Macia I.,Visual Communications Technologies Center | Macia I.,University of the Basque Country | Grana M.,University of the Basque Country | Maiora J.,University of the Basque Country | And 2 more authors.
Computers in Biology and Medicine | Year: 2011

Abdominal aortic aneurysm (AAA) is a condition where the weakening of the aortic wall leads to its widening and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAA can be treated non-invasively by means of the endovascular aneurysm repair technique (EVAR), consisting of placing a stent-graft inside the aorta by a cateter to exclude the aneurysm sac from the blood circulation. A major complication is the presence of liquid blood turbulences, called endoleaks, in the thrombus formed in the space between the aortic wall and the stent-graft. In this paper we propose an automatic method for the detection of type II endoleaks in computer tomography angiography (CTA) images. The lumen and thrombus in the aneurysm area are first segmented using a radial model approach. Then, these regions are split into Thrombus Connected Components (TCCs) using a watershed-based segmentation and geometric and image content-based characteristics are obtained for each TCC. Finally, TCCs are classified into endoleaks and non-endoleaks using a multilayer Perceptron (MLP) trained on manual labeled sample TCCs provided by experts. © 2011 Elsevier Ltd.


PubMed | Visual Communications Technologies Center
Type: Journal Article | Journal: Computers in biology and medicine | Year: 2011

Abdominal aortic aneurysm (AAA) is a condition where the weakening of the aortic wall leads to its widening and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAA can be treated non-invasively by means of the endovascular aneurysm repair technique (EVAR), consisting of placing a stent-graft inside the aorta by a cateter to exclude the aneurysm sac from the blood circulation. A major complication is the presence of liquid blood turbulences, called endoleaks, in the thrombus formed in the space between the aortic wall and the stent-graft. In this paper we propose an automatic method for the detection of type II endoleaks in computer tomography angiography (CTA) images. The lumen and thrombus in the aneurysm area are first segmented using a radial model approach. Then, these regions are split into Thrombus Connected Components (TCCs) using a watershed-based segmentation and geometric and image content-based characteristics are obtained for each TCC. Finally, TCCs are classified into endoleaks and non-endoleaks using a multilayer Perceptron (MLP) trained on manual labeled sample TCCs provided by experts.

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