Robust Point Set Registration Using Gaussian Mixture Models

Jian, Bing; Vemuri, Baba C.

doi:10.1109/tpami.2010.223

Cited by 755 publications

(165 citation statements)

References 58 publications

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“…To provide a quantitative comparison, we report the results of six state-of-the-art algorithms, such as shape context (SC) [23], TPS-RPM [3], RPM-LNS [13], GMMREG [19], CPD [11], and RPM- L 2 E [28] which were implemented using publicly available codes. The registration error of a pair of shapes is quantified as the average Euclidean distance between a point in the warped model shape and the ground truth corresponding point in the observed data shape (note that it is different from ε * defined in Eq.…”

Section: Resultsmentioning

confidence: 99%

“…The model points undergo a non-rigid transformation 𝒯 : IR D → IR D , and the goal is to estimate 𝒯, which warps the model points to the observed data points, so that the two point sets become aligned. In this paper, we will formulate the point registration as the estimation of a mixture of densities, where a Gaussian mixture model (GMM) is fitted to the observed data points Y , such that the GMM centroids of the Gaussian densities are constrained to coincide with the transformed model points 𝒯( X ) [11], [19], [12]. …”

Section: Methodsmentioning

confidence: 99%

“…The nearest point strategy of ICP can be replaced by soft assignments within a continuous optimization framework, e.g., the TPS-RPM [17], [3]. In the recent past, the point registration is typically solved by probabilistic methods [18], [19], [11], [12]. The Kernel Correlation Based Method [18] models each one of the two point sets as two probability distributions and measures the dissimilarity between the two distributions.…”

Section: Related Workmentioning

confidence: 99%

“…The Kernel Correlation Based Method [18] models each one of the two point sets as two probability distributions and measures the dissimilarity between the two distributions. It was later improved in [19], which represents the point sets using GMMs. In [11] and [12], the GMM is used to recast the point-to-point assignment problem into that of estimating the parameters of a mixture.…”

Section: Related Workmentioning

confidence: 99%

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Non-Rigid Point Set Registration by Preserving Global and Local Structures

Zhao

Yuille

2016

IEEE Trans. on Image Process.

271

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In previous work on point registration, the input point sets are often represented using Gaussian mixture models and the registration is then addressed through a probabilistic approach, which aims to exploit global relationships on the point sets. For non-rigid shapes, however, the local structures among neighboring points are also strong and stable and thus helpful in recovering the point correspondence. In this paper, we formulate point registration as the estimation of a mixture of densities, where local features, such as shape context, are used to assign the membership probabilities of the mixture model. This enables us to preserve both global and local structures during matching. The transformation between the two point sets is specified in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on both synthesized and real data show the robustness of our approach under various types of distortions such as deformation, noise, outliers, rotation and occlusion. It greatly outperforms state-of-the-art methods, especially when the data is badly degraded.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Non-Rigid Point Set Registration by Preserving Global and Local Structures

Zhao

Yuille

2016

IEEE Trans. on Image Process.

271

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“…The Gaussian mixture model (GMM) algorithm for point set registration was proposed by Jian et al [63]. These authors used the Gaussian mixture model to describe the distribution of the two given point sets M and S and treat the registration of two point sets as the alignment between two Gaussian mixtures by minimizing their L2-distance.…”

Section: Gmmmentioning

confidence: 99%

A Review of Point Feature Based Medical Image Registration

Guan

Wang

Cai

et al. 2018

Chin. J. Mech. Eng.

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Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms (PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However, to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeature-based methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.

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Global optimization point‐set registration based on translation/rotation decoupling for image‐guided surgery applications

Chen

Wang

2022

Medical Physics

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Purpose In image‐guided surgery systems, image‐to‐patient spatial registration is to get the spatial transformation between the image space and the actual operating space. Although the image‐to‐patient spatial registration methods using paired point or surface matching are used in some image‐guided neurosurgery systems, the key problem is that the global optimization registration result cannot be achieved. Therefore, this paper proposes a new rotation invariant feature for decoupling rotation and translation space, based on which global optimization point set registration method is proposed. Methods The new rotation invariant features, constructed based on the edges and the angles, are the rotation invariant, which has high feature resolution. Some of them are not only the rotation invariant, but also the translation invariant. To obtain the global optimal solution, branch and bound search strategy is used to search the parameter space of the translation and the computational cost is reduced simultaneously. The registration accuracy of the spatial registration method is analyzed by comparing the difference between the estimated transform and the standard transform to calculate the registration error. Results To validate the performance of the spatial registration method proposed, the registration performance was analyzed by comparing the experimental results with the results of the two mainstream registration methods (the iterative closest point [ICP] registration method and the coherent point drift method). In the experiments, the comparison was based on the registration accuracy and the execution time. We show our registration method can obtain higher accuracy in a shorter time in most cases. At the same time, when using ICP to further refine our results, the ICP method can converge in a very short time, which also shows that our method provides a good initial pose for the ICP method and can help the ICP converge to the global optimal solution faster. Our method can achieve an average rotation error of 0.124 degrees and an average translation error of 0.38 mm on 10 clinical data. Conclusions The results reveal that the surface registration method based on translation rotation decoupling can achieve superior performance regarding both the registration accuracy and the time efficiency in the image‐to‐patient spatial registration.

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Robust Point Set Registration Using Gaussian Mixture Models

Cited by 755 publications

References 58 publications

Non-Rigid Point Set Registration by Preserving Global and Local Structures

Non-Rigid Point Set Registration by Preserving Global and Local Structures

A Review of Point Feature Based Medical Image Registration

Global optimization point‐set registration based on translation/rotation decoupling for image‐guided surgery applications

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