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Elements of information theory
Elements of information theory
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Saliency, Scale and Image Description
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Multimodality Deformable Registration of Pre- and Intraoperative Images for MRI-guided Brain Surgery
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Incorporating Connected Region Labelling into Automatic Image Registration Using Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hybrid Image Registration based on Configural Matching of Scale-Invariant Salient Region Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 11 - Volume 11
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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IEEE Transactions on Information Theory
Likelihood maximization approach to image registration
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Reliability-driven, spatially-adaptive regularization for deformable registration
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
A robust hybrid method for nonrigid image registration
Pattern Recognition
Rigid registration of renal perfusion images using a neurobiology-based visual saliency model
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An information-theoretic method for multimodality medical image registration
Expert Systems with Applications: An International Journal
Multimodality image alignment using information-theoretic approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
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This paper presents a novel image similarity measure, referred to as quantitative-qualitative measure of mutual information (Q-MI), for multimodality image registration. Conventional information measures, e.g., Shannon's entropy and mutual information (MI), reflect quantitative aspects of information because they only consider probabilities of events. In fact, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Thus, it is important to consider both quantitative (i.e., probability) and qualitative (i.e., utility) measures of information in order to fully capture the characteristics of events. Accordingly, in multimodality image registration, Q-MI should be used to integrate the information obtained from both the image intensity distributions and the utilities of voxels in the images. Different voxels can have different utilities, for example, in brain images, two voxels can have the same intensity value, but their utilities can be different, e.g., a white matter (WM) voxel near the cortex can have higher utility than a WM voxel inside a large uniform WM region. In Q-MI, the utility of each voxel in an image can be determined according to the regional saliency value calculated from the scale-space map of this image. Since the voxels with higher utility values (or saliency values) contribute more in measuring Q-MI of the two images, the Q-MI-based registration method is much more robust, compared to conventional MI-based registration methods. Also, the Q-MI-based registration method can provide a smoother registration function with a relatively larger capture range. In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images.