2012
DOI: 10.1016/j.media.2010.03.005
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A review of 3D/2D registration methods for image-guided interventions

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Cited by 639 publications
(472 citation statements)
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“…3D/2D registration methods can be classified into three categories: extrinsic, intrinsic, or calibration‐based 21. The intrinsic methods can be further divided into two main categories, feature‐ and intensity‐based, and it is accepted that the latter has an important advantage over the former in that all available information can be utilized in the images.…”
Section: Methodsmentioning
confidence: 99%
“…3D/2D registration methods can be classified into three categories: extrinsic, intrinsic, or calibration‐based 21. The intrinsic methods can be further divided into two main categories, feature‐ and intensity‐based, and it is accepted that the latter has an important advantage over the former in that all available information can be utilized in the images.…”
Section: Methodsmentioning
confidence: 99%
“…e.g. (Pluim et al, 2003;Markelj et al, 2010;Glocker et al, 2011)), literature on registration On the left image, the coronary artery has been outlined using the da Vinci R touch screen display and is displayed on either the right or left eye. 3D telestration can be performed using a dual console setup, where the robot master manipulators not involved in instrument control can utilize a 3D arrow to demonstrate anatomic features.…”
Section: Intra-operative Registration For Augmented Reality Guidancementioning
confidence: 99%
“…Image fusion requires the images to be aligned such that structures being visible in each appear at the same spatial position. This transformation is called image registration and requires establishing a coordinate transform [16] by feature matching [2,3], optimizing a similarity measure [12,15] or by calibration-based approaches [13]. The latter relies on external tracking systems, pre-calibrated instruments or manual measurements.…”
Section: Introductionmentioning
confidence: 99%
“…These parameters can be enriched by camera tracking information to establish an efficient coordinate transformation [13]. Contrary to feature-based approaches, there is no need for solving potentially expensive correspondence problems or minimization of similarity measures.…”
Section: Introductionmentioning
confidence: 99%