Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
X-ray image categorization and retrieval using patch-based visual words representation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Local structure-based region-of-interest retrieval in brain MR images
IEEE Transactions on Information Technology in Biomedicine
Improving local descriptors by embedding global and local spatial information
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Image dissimilarity-based quantification of lung disease from CT
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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Content-based medical image retrieval is likely becoming an important tool to provide valuable information to assist physician to make critical diagnosis decisions. While most existing works perform the retrieval based on low-level visual features, the pathological spatial context, which is critical for analysis of the disease characteristics, has been less studied. We thus aim to effectively extract and represent the spatial context of pathological tissues, and design a novel hierarchical spatial matching (HSM) method based on the spatial pyramid matching. Our method is able to (1) handle the translation variations of the main pathological object; (2) describe the spatial information surrounding the pathological object in an adaptive scale; and (3) compute image similarities with an optimally weighted distance function. The proposed method shows better retrieval performance comparing to the other widely used techniques.