Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
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ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Endomicroscopic video retrieval using mosaicing and visual words
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Overview of the CLEF 2009 medical image annotation track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Evaluation of fast 2d and 3d medical image retrieval approaches based on image miniatures
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
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The present work introduces a 2D medical image retrieval system which employs interest points derived from superpixels in a bags of visual words (BVW) framework. BVWs rely on stable interest points so that the local descriptors can be clustered into representative, discriminative prototypes (the visual words). We show that using the centers of mass of superpixels as interest points yields higher retrieval accuracy when compared to using Difference of Gaussians (DoG) or a dense grid of interest points. Evaluation is performed on two data sets. The ImageCLEF 2009 data set of 14.400 radiographs is used in a categorization setting and the results compare favorable to more specialized methods. The second set contains 13 thorax CTs and is used in a hybrid 2D/3D localization task, localizing the axial position of the lung through the retrieval of representative 2D slices.