Journal ArticleDOI
Multimodality image registration by maximization of quantitative-qualitative measure of mutual information
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TLDR
The proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images, and can provide a smoother registration function with a relatively larger capture range.About:
This article is published in Pattern Recognition.The article was published on 2008-01-01. It has received 120 citations till now. The article focuses on the topics: Image registration & Mutual information.read more
Citations
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Journal ArticleDOI
DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting
Yangming Ou,Aristeidis Sotiras,Aristeidis Sotiras,Nikos Paragios,Nikos Paragios,Christos Davatzikos +5 more
TL;DR: A general-purpose deformable registration algorithm referred to as "DRAMMS" is presented, which extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each v oxel in a multi-scale and multi-resolution fashion.
Journal ArticleDOI
Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights
TL;DR: 12 general-purpose registration algorithms are evaluated, for their generality, accuracy and robustness, and the performances in light of algorithms' similarity metrics, transformation models and optimization strategies are discussed.
Journal ArticleDOI
Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context
TL;DR: The proposed method provided accurate registration and yielded better performance over standard registration methods as it incorporated spatial and geometric information via a 3D Harris operator on the registration between a high-resolution image and a low- resolution image.
Journal ArticleDOI
Self-similarity weighted mutual information: a new nonrigid image registration metric.
TL;DR: This work proposes a self-similarity weighted graph-based implementation of α-mutual information (α-MI) for nonrigid image registration and shows that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
Journal ArticleDOI
On Normalized Mutual Information: Measure Derivations and Properties
TL;DR: This paper derives alternative upper bounds and extends those to the case of two discrete random variables and normalized mutual information (NMI) measures are then obtained from those bounds, emphasizing the use of least upper bounds.
References
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A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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Elements of information theory
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.