Morphological descriptors are practical and essential biomarkers for diagnosis and/ntreatment selection for intracranial aneurysm management according to the current guidelines/nin use. Nevertheless, relatively little work has been dedicated to improve the three-dimensional/nquanti cation of aneurysmal morphology, automate the analysis, and hence reduce the inherent/nintra- and inter-observer variability of manual analysis. In this paper we propose a methodology/nfor the automated isolation and morphological quanti cation of saccular intracranial aneurysms/nbased on a 3D representation of the vascular anatomy.

This work was partially supported within the CENIT-CDTEAM and CENIT cvREMOD projects funded by the Spanish Ministry of Innovation and Science-CDTI and partly within the framework of the @neurIST Project (IST-2005-027703), which is co- nanced by the European Commission within the IST Program of the Sixth Framework Programm.