2D-Based 3D Volume Retrieval Using Singular Value Decomposition of Detected Regions
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | GFS2014 |
| Buchtitel: | Medical Computer Vision. Large Data in Medical Imaging |
| Serie: | Lecture Notes in Computer Science |
| Jahr: | 2014 |
| Seiten: | 185-195 |
| Verlag: | Springer International Publishing |
| Ort: | Nagoya, Japan |
| Organisation: | MICCAI |
| Abriss: | In this paper, a novel 3D retrieval model to retrieve medical volumes using 2D images as input is proposed. The main idea consists of applying a multi{scale detection of saliency of image regions. Then, the 3D volumes with the regions for each of the scales are associated with a set of projections onto the three canonical planes.The 3D shape is indirectly represented by a 2D{shape descriptor so that the 3D{shape matching is transformed into measuring similarity between 2D{shapes. The shape descriptor is de ned by the set of the k largest singular values of the 2D images and Euclidean distance between the vector descriptors is used as a similarity measure. The preliminary results obtained on a simple database show promising performance with a mean average precision (MAP) of 0.82 and could allow using the approach as part of a retrieval system in clinical routine. |
| Nutzerfelder: | MICCAI post-conference workshop: 26 Sept 2013 |
| Schlagworte: | 2D-based 3D retrieval, image retrieval, region detector, singular value decompasition |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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