Automated Detection and Classification of Intracranial Aneurysms based on 3D Surface Analysis

ii Intracranial aneurysms are localized, abnormal arterial dilations with a variable risk of rupture, which can lead to subarachnoid hemorrhage (SAH), a condition associated with high morbidity and mortality. The majority of non-traumatic SAH cases are caused by ruptured intracranial aneurysms. Accurate detection can decrease a significant proportion of misdiagnosed cases. However, only a small percent of all detected incidental aneurysms proceed to rupture and preventive treatment car-ries risks of complications, thus creating a need for aneurysm rupture risk stratification tools to help guide the treatment of these asymptomatic lesions. This research investigates the two major areas of intracranial aneurysm analysis- aneurysm detection and rupture risk classification. First a method for automatic detection of intracranial aneurysms is proposed. Applied to the segmented cerebral vasculature, the method detects aneurysms as suspect regions on the vascular tree, and is designed to assist diagnosticians with their interpretations. The method is imaging modality independent and was tested on magnetic resonance imaging, computed tomography and 3D angiography data. Sec-ond, a new approach to morphological analysis of the 3D shape of an aneurysm is presented as