Sparsity and scale: compact representations of deformation for diffeomorphic registration

Stefan Horst Sommer; Mads Nielsen; Xavier Pennec

doi:10.1109/MMBIA.2012.6164753

Sparsity and scale: compact representations of deformation for diffeomorphic registration

Stefan Horst Sommer, Mads Nielsen, Xavier Pennec

Datalogisk Institut

2 Citationer (Scopus)

Abstract

In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images.

Originalsprog	Engelsk
Titel	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA)
Antal sider	8
Forlag	IEEE
Publikationsdato	2012
Sider	217-224
ISBN (Trykt)	978-1-4673-0352-1
ISBN (Elektronisk)	978-1-4673-0353-8
DOI	https://doi.org/10.1109/MMBIA.2012.6164753
Status	Udgivet - 2012
Begivenhed	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) - Breckenridge, CO, USA Varighed: 9 jan. 2012 → 10 jan. 2012

Workshop

Workshop	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA)
Land/Område	USA
By	Breckenridge, CO
Periode	09/01/2012 → 10/01/2012

Adgang til dokumentet

10.1109/MMBIA.2012.6164753

Citationsformater

Sommer, SH , Nielsen, M & Pennec, X 2012, Sparsity and scale: compact representations of deformation for diffeomorphic registration. i 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE, s. 217-224 , 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), Breckenridge, CO, USA, 09/01/2012. https://doi.org/10.1109/MMBIA.2012.6164753

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AB - In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images.

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