Optimal data-driven sparse parameterization of diffeomorphisms for population analysis

Sandy Durrleman, Marcel Prastawa, Guido Gerig, Sarang Joshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
PublisherSpringer Verlag
Pages123-134
Number of pages12
ISBN (Print)9783642220913
DOIs
StatePublished - 2011
Event22nd International Conference on Information Processing in Medical Imaging, IPMI 2011 - Kloster Irsee, Germany
Duration: Jul 3 2011Jul 8 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6801 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
Country/TerritoryGermany
CityKloster Irsee
Period7/3/117/8/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Optimal data-driven sparse parameterization of diffeomorphisms for population analysis'. Together they form a unique fingerprint.

Cite this