NDLI: Registration of unseen images based on the generative manifold modeling of variations of appearance and anatomical shape in brain population

Content Provider	IEEE Xplore Digital Library
Author	Weiwei Zhang Davatzikos, C.
Copyright Year	2012
Description	Author affiliation: Section of Biomedical Image Analysis, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, 19104, USA (Weiwei Zhang; Davatzikos, C.)
Abstract	In this paper we propose a method to register a pair of images unseen to the original dataset based on a generative manifold model. The basic premise of this approach is to design an image distance metric using a weighted sum of similarity and smoothness terms derived from a diffeomorphic registration of pairwise images. A refined image distance matrix based on this metric can be adopted as an input for nonlinear dimensionality reduction of the dataset, and the learned manifold can be approximated to simultaneously reflect the variations of appearance and anatomical shape. The generative manifold model that combines the image distance measurement and the manifold learning technique is used to estimate the geodesic path via the unseen pair for composition of the final deformation field. The experimental result of a set of real 3D mouse brain volumes demonstrates that the estimated manifold coordinates appropriately reflect the trend in the original dataset and that the registration of unseen images using the shortest path inferred from the generative manifold model improves the result against the direct registration.
Starting Page	113
Ending Page	118
File Size	2686329
Page Count	6
File Format	PDF
ISBN	9781467303521
e-ISBN	9781467303545
e-ISBN	9781467303538
DOI	10.1109/MMBIA.2012.6164737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Measurement Shape Brain modeling Mice Kernel Image reconstruction
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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