Detection of cerebral aneurysms in MRA, CTA and 3D-RA data sets

We propose a system to automatically detect cerebral aneurysms in 3D X-ray rotational angiography images (3D-RA), magnetic resonance angiography images (MRA) and computed tomography angiography images (CTA). After image normalization, initial candidates are found by applying a blob-enhancing filter on the data sets. Clusters are computed by a modified k-means algorithm. A post-processing step reduces the false positive (FP) rate on the basis of computed features. This is implemented as a rule-based system that is adapted according to the modality. In MRA, clusters are excluded that are not neighbored to a vessel. As a final step, FP are further reduced by applying a threshold classification on a feature. Our method was tested on 93 angiographic data sets containing aneurysm and non-aneurysm cases. We achieved 95 % sensitivity with an average rate of 2.6 FP per data set (FP/DS) in case of 3D-RA, 89 % sensitivity at 6.6 FP/DS for MRA and 95 % sensitivity at 37.6 FP/DS with CTA, respectively. We showed that our post-processing approach eliminates FP in MRA with only a slight decrease of sensitivity. In contrast to other approaches, our algorithm does not require a vessel segmentation and does not require training of distributional properties.