Introduction to the Non-rigid Image Registration Evaluation Project (NIREP)

Christensen, Gary E.; Geng, Xiujuan; Kuhl, Jon G.; Bruss, Joel; Grabowski, Thomas J.; Pirwani, Imran A.; Vannier, Michael W.; Allen, John S.; Damásio, Hanna

doi:10.1007/11784012_16

Cited by 143 publications

(140 citation statements)

References 11 publications

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“…We have employed the NA0 Database [13] to demonstrate the capability of our method. In the dataset, there are 16 individual brain MR images, each of which has 32 manual anatomical labels.…”

Section: Resultsmentioning

confidence: 99%

Attribute Vector Guided Groupwise Registration

Yap

Shen

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Groupwise registration has been recently introduced for simultaneous registration of a group of images with the goal of constructing an unbiased atlas. To this end, direct application of information-theoretic entropy measures on image intensity has achieved various successes. However, simplistic voxelwise utilization of image intensity often neglects important contextual information, which can be provided by more comprehensive geometric and statistical features. In this paper, we employ attribute vectors, instead of image intensities, to guide groupwise registration. In particular, for each voxel, the attribute vector is computed from its multiple-scale neighborhoods to capture geometric information at different scales. Moreover, the probability density function (PDF) of each attribute in the vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure. For the purpose of registration, Jensen-Shannon (JS) divergence is used to measure the PDF dissimilarity of each attribute at corresponding locations of different individual images. By minimizing the overall JS divergence in the whole image space and estimating the deformation field of each image simultaneously, we can eventually register all images and build an unbiased atlas. Experimental results indicate that our method yields better registration quality, compared with a popular groupwise registration method.

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“…We have employed the NA0 Database [13] to demonstrate the capability of our method. In the dataset, there are 16 individual brain MR images, each of which has 32 manual anatomical labels.…”

Section: Resultsmentioning

confidence: 99%

Attribute Vector Guided Groupwise Registration

Yap

Shen

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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“…They can however be considered as a further evidence to support the value of our approach. A more advanced tool for benchmarking non-rigid registration algorithms might also be available in the future (Christensen et al, 2006). Fig.…”

Section: Experiments: Diffeomorphic Registration Can Be Fastmentioning

confidence: 99%

Diffeomorphic demons: Efficient non-parametric image registration

et al. 2009

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We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.

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“…Evaluating its accuracy has therefore become an issue of growing importance [1,2,3]. Contrary to rigid registration methods, validation of non-rigid methods remains a challenging task due to the absence of a gold standard [3].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 4D-CT Lung Registration

Kabus

Klinder

Murphy

et al. 2009

Lecture Notes in Computer Science

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Abstract. Non-rigid registration accuracy assessment is typically performed by evaluating the target registration error at manually placed landmarks. For 4D-CT lung data, we compare two sets of landmark distributions: a smaller set primarily defined on vessel bifurcations as commonly described in the literature and a larger set being well-distributed throughout the lung volume. For six different registration schemes (three in-house schemes and three schemes frequently used by the community) the landmark error is evaluated and found to depend significantly on the distribution of the landmarks. In particular, lung regions near to the pleura show a target registration error three times larger than nearmediastinal regions. While the inter-method variability on the landmark positions is rather small, the methods show discriminating differences with respect to consistency and local volume change. In conclusion, both a well-distributed set of landmarks and a deformation vector field analysis are necessary for reliable non-rigid registration accuracy assessment.

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Introduction to the Non-rigid Image Registration Evaluation Project (NIREP)

Cited by 143 publications

References 11 publications

Attribute Vector Guided Groupwise Registration

Attribute Vector Guided Groupwise Registration

Diffeomorphic demons: Efficient non-parametric image registration

Evaluation of 4D-CT Lung Registration

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