Multidirectional reconstruction of angiograms by stereogrammetry and computer

Vignaud, J; Rabischong, P.; Yver, J; Pardo, P.; Thurel, C

doi:10.1007/bf00346204

Cited by 14 publications

(3 citation statements)

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“…There is a large volume of literature on the subject of 3D vascular reconstruction. 15,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] One means of reconstructing pairs of 2D curves is to manually identify paired points on a pair of projection views. [18][19][20][21][22] This method has been used successfully to reconstruct the coronary circulation but, for the intracerebral circulation, is too laborious and difficult to be useful.…”

Section: A Other Approaches To 3d Curve Reconstructionmentioning

confidence: 99%

“…Several groups have therefore investigated 3D intracerebral vascular reconstruction from a pair of narrowly separated views. 15,20,21,[25][26][27] Creating accurate 3D curves from narrowly separated views is difficult in the presence of error, however. 14 Several investigators have described automatic reconstruction of lines or curves from a sequence of multiple, narrowly separated views.…”

Section: A Other Approaches To 3d Curve Reconstructionmentioning

confidence: 99%

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Three‐dimensional reconstruction of curves from pairs of projection views in the presence of error. I. Algorithms

1997

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We have previously described an approach to 3D intracerebral vascular reconstruction that uses an MRA as a reconstruction base. Additional vessels seen only by angiography are added by segmenting 2D curves from projection angiograms and reconstructing these curves into 3D, building upon the MRA. Intracerebral vascular reconstruction is difficult for at least two reasons. First, 2D curves must be associated on projection images even when the human eye cannot do so. Second, 3D curves must be reconstructed in the presence of errors such as misregistration, image distortion, and misdefinition of 2D curves. This paper is the first of two that address the specific issue of reconstruction of a 3D curve from a given pair of 2D curves in the presence of error. The method explicitly separates what can and cannot be determined from a pair of projection views. It is also capable of recognizing interruptions produced by viewplane errors, of continuing reconstruction beyond such interruptions, and of localizing and estimating the magnitude of the interruptions. These measurements can also be used to estimate the lengths of regional disparities between a pair of 2D curves, leading to a quantitative estimate of the capacity of a pair of 2D curves to combine to create a 3D object (match value). Match values can be used, in turn, as part of the strategy for automatically associating pairs of 2D curves. This paper provides methods for reconstructing a given pair of 2D curves into 3D in the presence of error and for calculating match values. Error analysis is given in the companion report.

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Section: A Other Approaches To 3d Curve Reconstructionmentioning

confidence: 99%

Section: A Other Approaches To 3d Curve Reconstructionmentioning

confidence: 99%

Three‐dimensional reconstruction of curves from pairs of projection views in the presence of error. I. Algorithms

1997

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“…Landmark-based methods [2, 161 have been demonstrated to be appropriate for a wide range of tasks, however they require the existence, specification, and accurate and consistent identification of localized landmark. Landmarks also usually only exist at sparse, discrete locations, thus they do not explicitly capture the intervening characteristics of continuous objects.…”

Section: Introductionmentioning

confidence: 99%

Intensity ridge and widths for tubular object segmentation and description

Aylward

Bullitt

Pizer

et al. 1996

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

116

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This paper introduces a technique for the automated description of tubular objects in 3 0 medical images. The goal of automated 30 object description is to extract a representation which consistently details the location, size, and structure of objects in 3 0 images using minimal user interaction. Such a representation provides a means by which objects can be classijied, quantijiably evaluated, and registered.It also serves as a region of interest specification for visualization processes.The technique presented in this paper is suited f o r generating representations of 30 objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated by its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects. This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 30 magnetic resonance angiographic images and bones from 3 0 X-ray computed tomographic images.

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