Sparse update for loopy belief propagation: fast dense registration for large state spaces
Date
2010
Authors
Xiao, Pengdong
Barnes, Nick
Lieby, Paulette
Caetano, Tiberio
Journal Title
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Volume Title
Publisher
IEEE Computer Society
Abstract
A dense point-based registration is an ideal starting point for detailed comparison between two neuroanatomical objects. This paper presents a new algorithm for global dense point-based registration between anatomical objects without assumptions about their shape. We represent mesh models of the surfaces of two similar 3D anatomical objects using a Markov Random Field and seek correspondence pairs between points in each shape. However, for densely sampled objects the set of possible point by point correspondences is very large. We solve the global non-rigid matching problem between the two objects in an efficient manner by applying loopy belief propagation. Typically loopy belief propagation is of order m3 for each iteration, where m is the number of nodes in a mesh. By avoiding computation of probabilities of configurations that cannot occur in practice, we reduce this to order m2. We demonstrate the method and its performance by registering hippocampi from a population of individuals aged 60-69. We find a corresponding rigid registration, and compare the results to a state-of-the-art technique and show comparable accuracy. Our method provides a global registration without prior information about alignment, and handles arbitrary shapes of spherical topology.
Description
Keywords
Keywords: Anatomical objects; Arbitrary shape; Global registration; Loopy belief propagation; Markov Random Fields; Mesh model; Nonrigid matching; Point correspondence; Point-based; Prior information; Rigid registration; Spherical topology; State space; Image match
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Source
Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2010)
Type
Conference paper
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2037-12-31