Standardized Evaluation of 2D-3D Registration

Kraats, Everine B. van de; Penney, Graeme P.; Tomaževič, Dejan; Walsum, Theo van; Niessen, Wiro J.

doi:10.1007/978-3-540-30135-6_70

Cited by 27 publications

(34 citation statements)

References 9 publications

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“…The RMS results are summarized in Table 1, including the following elements: CT: the segmented target CT dataset; R full : a short-scan reconstruction; R part : a partialscan reconstruction; R hbd : a hybrid reconstruction. The Table compares all three methods against the "ground truth" CT, and the partial-scan and hybrid methods against the short (2) CT Rpart (3) CT R hbd (4) R full Rpart (5) R full R hbd (4)- (5) / (4 …”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Although there is an extensive literature on rigid 2D/3D registration of x-ray images to preoperative CT models (e.g., [2,3,4]), much less has been published on deformable registration of x-rays to template bone models. The first work we have found is that of Fleute [5], who proposed a deformable 2D/3D method based on a statistical surface model.…”

Section: Related Workmentioning

confidence: 99%

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Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Sadowsky

Ramamurthi

Ellingsen

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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We present a method to improve the quality of cone-beam tomographic images computed from an intra-operative C-arm scan by adding information from an anatomical atlas. Limited range of C-arm view angles leads to reconstruction artifacts and poor anatomical detail. We propose to complete the missing views with simulated projections of a statistical anatomical model, which is deformably registered to match the data in the C-arm images. This paper presents the methods used to create the atlas and to register it with x-ray images. We compare the results of seven leave-one-out simulated hybrid reconstruction tests on a population of 13 subjects, with "ground-truth" CT, a classical short-scan, and a partial-scan reconstruction.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Sadowsky

Ramamurthi

Ellingsen

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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“…3. Scatter plot of the registration error [16] before (x-axis) and after (y-axis) the registration procedure, the dot size indicates the initial rotation.…”

Section: Resultsmentioning

confidence: 99%

Automatic Differentiation for GPU-Accelerated 2D/3D Registration

Grabner

Pock

Gross

et al. 2008

Advances in Automatic Differentiation

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Summary.A common task in medical image analysis is the alignment of data from different sources, e.g., X-ray images and computed tomography (CT) data. Such a task is generally known as registration. We demonstrate the applicability of automatic differentiation (AD) techniques to a class of 2D/3D registration problems which are highly computationally intensive and can therefore greatly benefit from a parallel implementation on recent graphics processing units (GPUs). However, being designed for graphics applications, GPUs have some restrictions which conflict with requirements for reverse mode AD, in particular for taping and TBR analysis. We discuss design and implementation issues in the presence of such restrictions on the target platform and present a method which can register a CT volume data set (512 × 512 × 288 voxels) with three X-ray images (512 × 512 pixels each) in 11.8 seconds on a GeForce 8800GTX graphics card.

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“…It has applications in object recognition, robotics and medical imaging, where in particular real-time solutions are required [van de Kraats et al, 2004, Gendrin et al, 2011.…”

Section: Introductionmentioning

confidence: 99%

A Saliency-based Framework for 2D-3D Registration

Brown

Guillemaut

Windridge

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:Here we propose a saliency-based filtering approach to the problem of registering an untextured 3D object to a single monocular image. The principle of saliency can be applied to a range of modalities and domains to find intrinsically descriptive entities from amongst detected entities, making it a rigorous approach to multi-modal registration. We build on the Kadir-Brady saliency framework due to its principled information-theoretic approach which enables us to naturally extend it to the 3D domain. The salient points from each domain are initially aligned using the SoftPosit algorithm. This is subsequently refined by aligning the silhouette with contours extracted from the image. Whereas other point based registration algorithms focus on corners or straight lines, our saliency-based approach is more general as it is more widely applicable e.g. to curved surfaces where a corner detector would fail. We compare our salient point detector to the Harris corner and SIFT keypoint detectors and show it generally achieves superior registration accuracy.

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Standardized Evaluation of 2D-3D Registration

Cited by 27 publications

References 9 publications

Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Automatic Differentiation for GPU-Accelerated 2D/3D Registration

A Saliency-based Framework for 2D-3D Registration

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