Name
Abstract | ||
|---|---|---|
We propose a new feature that can be used to automatically detect cerebral aneurysms in angiographic images. It combines both low-level and high-level features to a feature indicating aneurysms. The feature is used in a system for aneurysm detection in two types of magnetic resonance angiography (MRA) images and computed tomography angiography (CTA) images. The method was tested on 66 angiographic data sets containing aneurysm and non-aneurysm cases. We show that the newly introduced incorporation of the location based feature improves the detection quality. We achieve a sensitivity higher than 93% for all modalities with an average false positive rate varying from 8.8 to 20.9 per data set, depending on the modality. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISBI.2012.6235669 | Biomedical Imaging |
Keywords | Field | DocType |
biomedical MRI,blood vessels,brain,computerised tomography,image recognition,medical image processing,CTA images,MRA images,aneurysm features,angiographic images,automatic aneurysm detection,cerebral aneurysm automatic detection,computed tomography angiography,high level features,low level features,magnetic resonance angiography images,Aneurysm,Angiography,Computer aided Diagnosis,Object detection | Object detection,Computer vision,False positive rate,Pattern recognition,Computed tomography angiography,Computer science,Computer-aided diagnosis,Aneurysm,Feature extraction,Artificial intelligence,Magnetic resonance angiography,Angiography | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4577-1857-1 | 1 |
PageRank | References | Authors |
0.40 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clemens M. Hentschke | 1 | 1 | 0.40 |
| Klaus D. Tönnies | 2 | 18 | 2.40 |
| O. Beuing | 3 | 122 | 15.70 |
| Rosa Nickl | 4 | 1 | 0.40 |