Deformable Vessel-Based Registration Using Landmark-Guided Coherent Point Drift

Hu, Yipeng; Rijkhorst, Erik-Jan; Manber, Richard; Hawkes, David J.; Barratt, Dean C.

doi:10.1007/978-3-642-15699-1_7

Cited by 29 publications

(24 citation statements)

References 16 publications

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“…9,10 4414*i {, combined landmark and intensity information to guide the registration. Another group working with a hybrid method is Chen et al 4 Conceptually, most similar to our work is the method presented by Hu et al, 15 who used the CPD with landmarks to aid the CPD registration process. We have also previously used the CPD to register 3D vessel centerline points, obtained by a camera multi-view system, 5 to centerline points of an MRA image.…”

Section: Introductionmentioning

confidence: 93%

“…If the vessels have missing data, however, the results are degraded. For this reason we have also incorporated landmark information, but in a rather different fashion as used by Hu et al 15 In our case the CPD is only used once to obtain the initial correspondences between the vessel centerline points. Then an iterative relaxation process is used to minimize errors, using the TPS semilandmarks method.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

Marreiros

Wang

Rossitti

2016

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling

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In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). The method was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to match vessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-Plate Spline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, while the semilandmark method iteratively relaxes/slides the points.For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were applied to the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where a few control points were manipulated to obtain the desired transformation (T 1 ). Once the correspondences are known, the corresponding points are used to define a new TPS deformation(T 2 ). The errors are measured in the deformed space, by transforming the original points using T 1 and T 2 and measuring the distance between them. To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and then deformed. Furthermore, anisotropic normally distributed noise was added.The results show that the error estimates (root mean square error and mean error) are below 1 mm, even in the presence of noise and incomplete data.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 98%

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

Marreiros

Wang

Rossitti

2016

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling

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“…Myronenko et al consider the alignment of two point sets as a probability density estimation [15] and they call the method Coherent Point Drift (CPD). The CPD approach is extended in [22], [23], [24], [25]. Dai et al proposed a hierarchical parts-based CPD-LB morphing framework to avoid underfitting and over-fitting [14].…”

Section: Related Workmentioning

confidence: 99%

A Data-Augmented 3D Morphable Model of the Ear

Dai

Pears

Smith

2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.

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“…Recently, several variants of CPD have been proposed to meet specific requirements. Hu et al [10] added landmark information in the registration, while [11] and [12] focused on automatic parameter selection and outlier modeling.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid point set registration for Chinese characters using structure-guided coherent point drift

Sun

Lian

Tang

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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This paper proposes a non-rigid point set registration method called Structure-Guided Coherent Point Drift (SGCPD). The key idea of our method is to utilize structural information and combine the global and local point registrations together to improve the original Coherent Point Drift (CPD) algorithm. Specifically, given two point sets, we first align them using the CPD method with Localized Operator (CPDLO). Then we divide the target point set into several subsets and apply CPDLO to each subset. Finally, we implement the above two procedures until convergence. In this manner, more detailed information can be well exploited and thus higher registration accuracy can be achieved. Experimental results demonstrate that our method outperforms the original CPD approach on both point registration accuracy and skeleton decomposition accuracy for Chinese characters.

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Deformable Vessel-Based Registration Using Landmark-Guided Coherent Point Drift

Cited by 29 publications

References 16 publications

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

A Data-Augmented 3D Morphable Model of the Ear

Non-rigid point set registration for Chinese characters using structure-guided coherent point drift

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