Title

Shape Registration By Simultaneously Optimizing Representation And Transformation

Abstract

This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods based on landmarks-sliding, its advantages include: (1) It can naturally deal with shapes of complex topologies and 3D dimension; (2) It is more robust against data noise; (3) The registration performance is better in terms of the generalization error of the resultant statistical shape model. These are demonstrated on both synthetic and biomedical shapes. © Springer-Verlag Berlin Heidelberg 2007.

Publication Date

1-1-2007

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

4792 LNCS

Issue

PART 2

Number of Pages

809-817

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-75759-7_98

Socpus ID

84883836472 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883836472

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