A novel non-rigid registration method based on non-parametric statistical deformation model for medical image analysis
A novel non-rigid registration method based on non-parametric statistical deformation model for medical image analysis
Non-rigid registration has been widely used in medical image processing for many years. In order to preserve the anatomical topology and perform the registration more realistically and reliably for image guided surgery, methods based on statistical deformation model have been receiving considerable interests. However, the shortcomings in previous work such as the empirically configured weighting parameter for the statistical term lead to a controversial and unrealistic alignment. Therefore, a non-parametric method based on statistical deformation model is proposed here to avoid the discussion of weighting parameter. Our novel method is developed through incorporating the statistical model into two indispensable terms: similarity metric and smoothing regulariser. The advantages of the proposed algorithm in terms of convergence rate and registration accuracy have been proved mathematically in methodology and evaluated numerically in experiments compared with the state of the art method. It has also laid a solid foundation for the development of multi-modality image fusion with prior knowledge in the future.
statistical deformation model, non-rigid registration, nonparametric model, nuclear medicine imaging
Cui, Zheng
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Mahmoodi, Sasan
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Conway, Joy
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Guy, Matthew
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Lewis, Emma
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Havelock, Tom
e02dcc9e-aee2-4ee9-ae96-6386cdf0a13f
Bennett, Michael
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Cui, Zheng
b0127743-2b0d-4fd7-a383-fa6ed3ccff59
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
04b19151-4c9a-48f8-b594-c224c699c45c
Guy, Matthew
1a40b2ed-3aec-4fce-9954-396840471c28
Lewis, Emma
932d0fdf-d3d5-40f0-9ed2-7a4ca1fd3e74
Havelock, Tom
e02dcc9e-aee2-4ee9-ae96-6386cdf0a13f
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Cui, Zheng, Mahmoodi, Sasan, Conway, Joy, Guy, Matthew, Lewis, Emma, Havelock, Tom and Bennett, Michael
(2017)
A novel non-rigid registration method based on non-parametric statistical deformation model for medical image analysis.
In IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) 2017.
IEEE.
3 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Non-rigid registration has been widely used in medical image processing for many years. In order to preserve the anatomical topology and perform the registration more realistically and reliably for image guided surgery, methods based on statistical deformation model have been receiving considerable interests. However, the shortcomings in previous work such as the empirically configured weighting parameter for the statistical term lead to a controversial and unrealistic alignment. Therefore, a non-parametric method based on statistical deformation model is proposed here to avoid the discussion of weighting parameter. Our novel method is developed through incorporating the statistical model into two indispensable terms: similarity metric and smoothing regulariser. The advantages of the proposed algorithm in terms of convergence rate and registration accuracy have been proved mathematically in methodology and evaluated numerically in experiments compared with the state of the art method. It has also laid a solid foundation for the development of multi-modality image fusion with prior knowledge in the future.
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A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis
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Accepted/In Press date: 21 October 2017
Venue - Dates:
2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, Hyatt Regency , Atlanta, United States, 2017-10-21 - 2017-10-28
Keywords:
statistical deformation model, non-rigid registration, nonparametric model, nuclear medicine imaging
Identifiers
Local EPrints ID: 412272
URI: http://eprints.soton.ac.uk/id/eprint/412272
PURE UUID: f61aafe6-c10f-47e7-913b-deee3fa5424c
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Date deposited: 17 Jul 2017 13:19
Last modified: 21 Sep 2024 02:15
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Contributors
Author:
Zheng Cui
Author:
Sasan Mahmoodi
Author:
Joy Conway
Author:
Matthew Guy
Author:
Emma Lewis
Author:
Tom Havelock
Author:
Michael Bennett
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