Random Spatial Structure of Geometric Deformations and Bayesian Nonparametrics

Abstract : Our work is motivated by the geometric study of lower back pain from patient CT images. In this paper, we take a first step towards that goal by introducing a data-driven way of identifying anatomical regions of interest. We propose a probabilistic model of the geometrical variability and describe individual patients as noisy deformations of a random spatial structure (modeled as regions) from a common template. The random regions are generated using the distance dependent Chinese Restaurant Process. We employ the Gibbs sampler to infer regions from a set of noisy deformation fields. Each step of the sampler involves model selection (Bayes factor) to decide about fusing regions. In the discussion, we highlight connections between image registration and Markov chain Monte Carlo methods.
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Communication dans un congrès
GSI - Geometric Science of Information - 2013, Aug 2013, Paris, France. Springer, 8085, pp.120-127, 2013, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-642-40020-9_12〉
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Soumis le : lundi 22 juillet 2013 - 22:16:20
Dernière modification le : jeudi 11 janvier 2018 - 16:19:02
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Christof Seiler, Xavier Pennec, Susan Holmes. Random Spatial Structure of Geometric Deformations and Bayesian Nonparametrics. GSI - Geometric Science of Information - 2013, Aug 2013, Paris, France. Springer, 8085, pp.120-127, 2013, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-642-40020-9_12〉. 〈hal-00847185〉

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