2011
DOI: 10.1007/978-3-642-25899-2_8
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Variational Surface Reconstruction from Sparse and Nonparallel Contours for Freehand 3D Ultrasound

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Cited by 2 publications
(2 citation statements)
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“…For example, a 2-D non-rigid registration algorithm establishes a spatial correspondence between neighboring B-scans using B-splines (Rueckert et al 1999), and is applied for the interpolation of B-scan pixels (Penney et al 2004). A variational interpolation method for reconstructing the volume of interest (VOI) directly from contours segmented from the original freehand B-scans in the pre-processing step was proposed in a research study (Deng et al 2011(Deng et al , 2012. Kernel regression can be applied as a two-step reconstruction algorithm that consists of bin-filling and finding a nonparametric estimation for all of the volume data from the previous sample of sparse data (Chen et al 2014).…”
Section: Reconstructionmentioning
confidence: 99%
“…For example, a 2-D non-rigid registration algorithm establishes a spatial correspondence between neighboring B-scans using B-splines (Rueckert et al 1999), and is applied for the interpolation of B-scan pixels (Penney et al 2004). A variational interpolation method for reconstructing the volume of interest (VOI) directly from contours segmented from the original freehand B-scans in the pre-processing step was proposed in a research study (Deng et al 2011(Deng et al , 2012. Kernel regression can be applied as a two-step reconstruction algorithm that consists of bin-filling and finding a nonparametric estimation for all of the volume data from the previous sample of sparse data (Chen et al 2014).…”
Section: Reconstructionmentioning
confidence: 99%
“…This method takes a point cloud with oriented point normals as its input and creates an indicator function (an inside outside table) which it can then use to determine connectivity of input points and extract a 3D model. Other closely related works include (Mullen et al, 2010) and work by Deng in (Deng et al, 2011) which use a variational approach, but either depend on denser samples or completely closed contours as input.…”
Section: Related Workmentioning
confidence: 99%