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Estimation of the uncertainty of elastic image registration with the demons algorithm.

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Title: Estimation of the uncertainty of elastic image registration with the demons algorithm.
Authors: Hub, M .1 martina.hub@physiologie.uni-heidelberg.de, Karger, C. P.1
Source: Physics in Medicine & Biology. 2013, Vol. 58 Issue 9, p3023-3036. 14p.
Subjects: Image registration, Voxel-based morphometry, Algorithms, Deformations (Mechanics), Standard deviations
Abstract: The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate. [ABSTRACT FROM AUTHOR]
© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 90049806
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Estimation of the uncertainty of elastic image registration with the demons algorithm.
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  Data: <searchLink fieldCode="AR" term="%22Hub%2C+M+%2E%22">Hub, M .</searchLink><relatesTo>1</relatesTo><i> martina.hub@physiologie.uni-heidelberg.de</i><br /><searchLink fieldCode="AR" term="%22Karger%2C+C%2E+P%2E%22">Karger, C. P.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Physics+in+Medicine+%26+Biology%22">Physics in Medicine & Biology</searchLink>. 2013, Vol. 58 Issue 9, p3023-3036. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Voxel-based+morphometry%22">Voxel-based morphometry</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Deformations+%28Mechanics%29%22">Deformations (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved</i> (Copyright applies to all Abstracts.)
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        Value: 10.1088/0031-9155/58/9/3023
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 3023
    Subjects:
      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Voxel-based morphometry
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Deformations (Mechanics)
        Type: general
      – SubjectFull: Standard deviations
        Type: general
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      – TitleFull: Estimation of the uncertainty of elastic image registration with the demons algorithm.
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            NameFull: Hub, M .
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            NameFull: Karger, C. P.
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              Text: 2013
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              Y: 2013
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              Value: 58
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              Value: 9
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            – TitleFull: Physics in Medicine & Biology
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