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Conference Papers Information processing in medical imaging : proceedings of the ... conference. Year : 2011

Optimal data-driven sparse parameterization of diffeomorphisms for population analysis.

Abstract

In this paper, we propose a novel approach for intensity based atlas construction from a population of anatomical images, that estimates not only a template representative image but also a common optimal parameterization of the anatomical variations evident in the population. First, we introduce a discrete parameterization of large diffeomorphic deformations based on a finite set of control points, so that deformations are characterized by a low dimensional geometric descriptor. Second, we optimally estimate the position of the control points in the template image domain. As a consequence, control points move to where they are needed most to capture the geometric variability evident in the population. Third, the optimal number of control points is estimated by using a log - L1 sparsity penalty. The estimation of the template image, the template-to-subject mappings and their optimal parameterization is done via a single gradient descent optimization, and at the same computational cost as independent template-to-subject registrations. We present results that show that the anatomical variability of the population can be encoded efficiently with these compact and adapted geometric descriptors.

Domains

Medical Imaging

Dates and versions

hal-00818405 , version 1 (26-04-2013)

Identifiers

Cite

Stanley Durrleman, Marcel Prastawa, Guido Gerig, Sarang Joshi. Optimal data-driven sparse parameterization of diffeomorphisms for population analysis.. 22nd International Conference, IPMI 2011, Jul 2011, Kloster Irsee, Germany. pp.123-34, ⟨10.1007/978-3-642-22092-0_11⟩. ⟨hal-00818405⟩
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