Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Endomicroscopic image retrieval and classification using invariant visual features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
Towards the improvement of textual anatomy image classification using image local features
MMAR '11 Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
Interpreting endomicroscopic images is still a significant challenge, especially since one single still image may not always contain enough information to make a robust diagnosis. To aid the physicians, we investigated some local feature-based retrieval methods that provide, given a query image, similar annotated images from a database of endomicroscopic images combined with high-level diagnosis represented as textual information. Local feature-based methods may be limited by the small field of view (FOV) of endomicroscopy and the fact that they do not take into account the spatial relationship between the local features, and the time relationship between successive images of the video sequences. To extract discriminative information over the entire image field, our proposed method collects local features in a dense manner instead of using a standard salient region detector. After the retrieval process, we introduce a verification step driven by the textual information in the database and in which spatial relationship between the local features is used. A spatial criterion is built from the co-occurence matrix of local features and used to remove outliers by thresholding on this criterion. To overcome the small-FOV problem and take advantage of the video sequence, we propose to combine image retrieval and mosaicing. Mosaicing essentially projects the temporal dimension onto a large field of view image. In this framework, videos, represented by mosaics, and single images can be retrieved with the same tools. With a leave-n-out cross-validation, our results show that taking into account the spatial relationship between local features and the temporal information of endomicroscopic videos by image mosaicing improves the retrieval accuracy.