2D-Based 3D Volume Retrieval Using Singular Value Decomposition of Detected Regions
| Type of publication: | Inproceedings |
| Citation: | GFS2014 |
| Booktitle: | Medical Computer Vision. Large Data in Medical Imaging |
| Series: | Lecture Notes in Computer Science |
| Year: | 2014 |
| Pages: | 185-195 |
| Publisher: | Springer International Publishing |
| Location: | Nagoya, Japan |
| Organization: | MICCAI |
| Abstract: | 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. |
| Userfields: | MICCAI post-conference workshop: 26 Sept 2013 |
| Keywords: | 2D-based 3D retrieval, image retrieval, region detector, singular value decompasition |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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