The University of Southampton
University of Southampton Institutional Repository

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
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
IEEE
Cui, Zheng
b0127743-2b0d-4fd7-a383-fa6ed3ccff59
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
04b19151-4c9a-48f8-b594-c224c699c45c
Guy, Matthew
7129da19-33af-4721-a69a-0a9ab1c455e3
Lewis, Emma
932d0fdf-d3d5-40f0-9ed2-7a4ca1fd3e74
Havelock, Tom
e02dcc9e-aee2-4ee9-ae96-6386cdf0a13f
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Cui, Zheng
b0127743-2b0d-4fd7-a383-fa6ed3ccff59
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
04b19151-4c9a-48f8-b594-c224c699c45c
Guy, Matthew
7129da19-33af-4721-a69a-0a9ab1c455e3
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.

Text
A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 21 October 2017
Venue - Dates: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, Hyatt Regency, 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

Catalogue record

Date deposited: 17 Jul 2017 13:19
Last modified: 06 Oct 2020 18:56

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×