NDLI: Feature-based Alignment of Volumetric Multi-modal Images

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Feature-based Alignment of Volumetric Multi-modal Images

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Feature-based Alignment of Volumetric Multi-modal Images

Content Provider	PubMed Central
Author	Toews, Matthew Lilla, Zöllei Wells, William M.
Abstract	This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.
Starting Page	25
File Format	PDF
ISSN	10112499
Journal	Information processing in medical imaging : proceedings of the ... conference
Volume Number	23
Language	English
Publisher Date	2013-01-01
Access Restriction	Open
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine

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