Hip Prosthesis Design

Cinquin, P.; Chalmond, Bernard; Berard, D.; Dusserre, L.; Trouilloud, P.; Grammont, P; Binnert, D; Mabille, J. P.; Carasso, C.

doi:10.1007/978-3-642-93201-4_35

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…The simplest representations for shape are basic geometric objects, such as ellipses (Cinquin, Chalmond and Berard 1982;Amenta, Bern and Kamvysselis 1998;Rossi and Willsky 2003), polygons (Malladi, Sethian and Vemuri 1994;Malladi, Sethian and Vemuri 1995;Sederberg, Gao, Wang and Mu 1993;Sato, Wheeler and Ikeuchi 1997), and slightly more involved specifications such as superellipsoids (Gong, Pathak, Haynor, Cho and Kim 2004).…”

Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical modeling of simply connected 2D shapes

Gu,

Pati,

Dunson

2012

Preprint

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Models for distributions of shapes contained within images can be widely used in biomedical applications ranging from tumor tracking for targeted radiation therapy to classifying cells in a blood sample. Our focus is on hierarchical probability models for the shape and size of simply connected 2D closed curves, avoiding the need to specify landmarks through modeling the entire curve while borrowing information across curves for related objects. Prevalent approaches follow a fundamentally different strategy in providing an initial point estimate of the curve and/or locations of landmarks, which are then fed into subsequent statistical analyses. Such two-stage methods ignore uncertainty in the first stage, and do not allow borrowing of information across objects in estimating object shapes and sizes. Our fully Bayesian hierarchical model is based on multiscale deformations within a linear combination of cyclic basis characterization, which facilitates automatic alignment of the different curves accounting for uncertainty. The characterization is shown to be highly flexible in representing 2D closed curves, leading to a nonparametric Bayesian prior with large support.Efficient Markov chain Monte Carlo methods are developed for simultaneous analysis of many objects. The methods are evaluated through simulation examples and applied to yeast cell imaging data.

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Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical modeling of simply connected 2D shapes

Gu,

Pati,

Dunson

2012

Preprint

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“…On the other hand, the direct use of deformable contours in image reconstruction has been very limited because of the nonlinearity of the direct model, relating the surface parameters and the projection values. For those applications, explicit contour models are mostly parametric, and often involve a very few parameters [5]- [8].…”

mentioning

confidence: 99%

“…The pioneering contributions involve very simple shapes, as a set of 2-D parallel ellipses for 3-D vascular tree modeling in angiographic images [5]- [7]. Handling an image formed of elliptic objects is a simple task, since computation of its 2-D projections is direct and analytic [15].…”

mentioning

confidence: 99%

Polygonal and Polyhedral Contour Reconstruction in Computed Tomography

Soussen

Mohammad-Djafari

2004

IEEE Trans. on Image Process.

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Abstract-This paper is about three-dimensional (3-D) reconstruction of a binary image from its X-ray tomographic data. We study the special case of a compact uniform polyhedron totally included in a uniform background and directly perform the polyhedral surface estimation. We formulate this problem as a nonlinear inverse problem using the Bayesian framework. Vertice estimation is done without using a voxel approximation of the 3-D image. It is based on the construction and optimization of a regularized criterion that accounts for surface smoothness. We investigate original deterministic local algorithms, based on the exact computation of the line projections, their update, and their derivatives with respect to the vertice coordinates. Results are first derived in the two-dimensional (2-D) case, which consists of reconstructing a 2-D object of deformable polygonal contour from its tomographic data. Then, we investigate the 3-D extension that requires technical adaptations. Simulation results illustrate the performance of polygonal and polyhedral reconstruction algorithms in terms of quality and computation time.Index Terms-Block relaxation algorithms, closed contour and surface reconstruction, deformable template, global and local iterative descent algorithms, polygonal and polyhedral modeling, two-dimensional and three-dimensional binary image reconstruction from projections, X-ray tomography.

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Individual hip prosthesis design from CT images

Chalmond

1988

Pattern Recognition Letters

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Hip Prosthesis Design

Cited by 6 publications

References 1 publication

Bayesian hierarchical modeling of simply connected 2D shapes

Bayesian hierarchical modeling of simply connected 2D shapes

Polygonal and Polyhedral Contour Reconstruction in Computed Tomography

Individual hip prosthesis design from CT images

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