Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911212 | Proceedings of SPIE |
Keywords | Field | DocType |
Aneurysm detection,Computer aided Diagnosis,Angiographies,MRA,CTA,3D-RA | Rotational angiography,Nuclear medicine,Data set,Normalization (statistics),Computed tomography angiography,Artificial intelligence,Angiography,Vessel segmentation,Computer vision,Aneurysm,Medical physics,Magnetic resonance angiography,Physics | Conference |
Volume | ISSN | Citations |
8315 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clemens M. Hentschke | 1 | 5 | 1.99 |
| O. Beuing | 2 | 122 | 15.70 |
| rosa nickl | 3 | 0 | 0.34 |
| klaus d tonnies | 4 | 3 | 1.44 |