Least median of squares filtering of locally optimal point matches for compressible flow image registration

Edward Castillo, Richard Castillo, Benjamin White, Javier Rojo, Thomas Guerrero

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.

Original languageEnglish (US)
Pages (from-to)4827-4833
Number of pages7
JournalPhysics in Medicine and Biology
Volume57
Issue number15
DOIs
StatePublished - Aug 7 2012
Externally publishedYes

Fingerprint

Least-Squares Analysis
Thorax
Noise

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Least median of squares filtering of locally optimal point matches for compressible flow image registration. / Castillo, Edward; Castillo, Richard; White, Benjamin; Rojo, Javier; Guerrero, Thomas.

In: Physics in Medicine and Biology, Vol. 57, No. 15, 07.08.2012, p. 4827-4833.

Research output: Contribution to journalArticle

Castillo, Edward ; Castillo, Richard ; White, Benjamin ; Rojo, Javier ; Guerrero, Thomas. / Least median of squares filtering of locally optimal point matches for compressible flow image registration. In: Physics in Medicine and Biology. 2012 ; Vol. 57, No. 15. pp. 4827-4833.
@article{0c5133d818934e00bc7cf3e9372168ea,
title = "Least median of squares filtering of locally optimal point matches for compressible flow image registration",
abstract = "Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.",
author = "Edward Castillo and Richard Castillo and Benjamin White and Javier Rojo and Thomas Guerrero",
year = "2012",
month = "8",
day = "7",
doi = "10.1088/0031-9155/57/15/4827",
language = "English (US)",
volume = "57",
pages = "4827--4833",
journal = "Physics in Medicine and Biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd.",
number = "15",

}

TY - JOUR

T1 - Least median of squares filtering of locally optimal point matches for compressible flow image registration

AU - Castillo, Edward

AU - Castillo, Richard

AU - White, Benjamin

AU - Rojo, Javier

AU - Guerrero, Thomas

PY - 2012/8/7

Y1 - 2012/8/7

N2 - Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.

AB - Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.

UR - http://www.scopus.com/inward/record.url?scp=84863967319&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863967319&partnerID=8YFLogxK

U2 - 10.1088/0031-9155/57/15/4827

DO - 10.1088/0031-9155/57/15/4827

M3 - Article

C2 - 22797602

AN - SCOPUS:84863967319

VL - 57

SP - 4827

EP - 4833

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 15

ER -