Effective Incorporation of Spatial Information in a Mutual Information Based 3D-2D Registration of a CT Volume to X-Ray Images

Zheng, Guoyan

doi:10.1007/978-3-540-85990-1_111

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Moreover, the addition of spatial information into the similarity estimation, as it is e.g. done with higher-order densities [22], neighborhood patches [23], conditional mutual information [13], local volume densities [37], Markov random fields [36], and spatial-context mutual information [35], leads to improvements. The i.i.d.…”

Section: Iid Coordinate Samplesmentioning

confidence: 98%

A contextual maximum likelihood framework for modeling image registration

Wachinger

Navab

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive information of each image. This extension has multiple advantages. It allows for a unified description of geometric and iconic registration, with the consequential analysis of similarities. It enables to arrange registration techniques in a continuum, limited by pure intensityand feature-based registration. With this wide spectrum of techniques combined, we can model hybrid registration approaches. The probabilistic coupling allows further to deduce optimal descriptors and to model the adaptation of description layers during the process, as it is done for joint registration/segmentation. Finally, we deduce a new registration algorithm that allows for a dynamic adaptation of the description layers during the registration. Excellent results confirm the advantages of the new registration method, the major contribution of this article lies, however, in the theoretical analysis.

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Section: Iid Coordinate Samplesmentioning

confidence: 98%

A contextual maximum likelihood framework for modeling image registration

Wachinger

Navab

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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“…The estimated rigid transformation is then treated as the starting value for the next step, i.e., the intensity-based 2D-3D image registration. In this study, we used a spline-based multi-resolution 2D-3D image registration algorithm [29] incorporating a roust similarity measure that is derived from information and Markov random field theories [30], allowing for effective incorporation of spatial information into the intensity-based 2D-3D image registration. In the following, the details about both steps are given.…”

Section: Hybrid 2d-3d Registrationmentioning

confidence: 99%

“…Our intensity-based 2D-3D registration scheme is based on a recently introduced spline-based multi-resolution 2D-3D registration scheme [29] but with a different similarity measure. We use a similarity measure that is derived from information and Markov random field theories [30]. It allows us to effectively incorporate spatial information and has following form,…”

Section: Intensity-based 2d-3d Registrationmentioning

confidence: 99%

Computer assisted determination of acetabular cup orientation using 2D–3D image registration

Zheng

Zhang

2010

Int J CARS

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“…With regard to registration of medical images, spatial information is very important and should be incorporated into grayscale-based based registration algorithms. A 3D-2D registration of CT and X-ray images incorporated the spatial information in a variational approximation and obtained a high registration accuracy [11]. Positions with large gradient usually correspond to tissue transition, which provides spatial information [12].…”

Section: Introductionmentioning

confidence: 99%

A Stationary Wavelet Transform Based Approach to Registration of Planning CT and Setup Cone beam-CT Images in Radiotherapy

et al. 2014

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Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).

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Effective Incorporation of Spatial Information in a Mutual Information Based 3D-2D Registration of a CT Volume to X-Ray Images

Cited by 7 publications

References 12 publications

A contextual maximum likelihood framework for modeling image registration

A contextual maximum likelihood framework for modeling image registration

Computer assisted determination of acetabular cup orientation using 2D–3D image registration

A Stationary Wavelet Transform Based Approach to Registration of Planning CT and Setup Cone beam-CT Images in Radiotherapy

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