The ellipsoidal skeleton in medical applications

Banegas, Frédéric; Jaeger, Marc; Michelucci, Dominique; Roelens, Marc

doi:10.1145/376957.376961

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…From these local variations, we can obtain treatment margins in all directions, however, we only take the anterior-posterior (AP), left-right (LR), and superior-inferior (SI) directions, because only these directions are commonly considered in clinical practice, and in order to compare our results with other studies existing in the literature relative to variability in the radiotherapy process, we will take the best-fitting ellipsoid (see, for instance, [7,8] to the standard deviation function SD u . Then, from the axis of the ellipsoid and the corresponding transformation (from the maximum values) to the coordinate axis, we also obtain the AP, LR, and SI variations.…”

Section: Mean Surfaces and Standard Deviationsmentioning

confidence: 99%

Quantifying Uncertainties for Prostate Image-Guided Radiotherapy: A 3D Organ Reconstruction and Registration Method

Gual‐Arnau

Maso²,

Lliso³

et al. 2009

TOMIJ

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Abstract:The purpose of this paper is to present a method for volumetric reconstruction, registration and margin assignation applicable to both conventional CT scans and on board CT imaging. This method does not depend on the shape of the organs, the bony anatomy or the use of markers, and we apply it to prostate and bladder. 3D reconstructions are performed by means of spline surfaces and the 3D reconstructed surfaces are registered to a planning surface, using a multidimensional alignment from the Euclidean distance transform and the Levenberg-Marquardt optimization algorithm. Once the reconstructed surfaces are registered, we define a mean surface and obtain the corresponding variances from this mean surface. The method works properly and demonstrates that once translations are insulated by registration, residual uncertainties can be handled with the margin assigned for delineation variation and organ deformation.

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Section: Mean Surfaces and Standard Deviationsmentioning

confidence: 99%

Quantifying Uncertainties for Prostate Image-Guided Radiotherapy: A 3D Organ Reconstruction and Registration Method

Gual‐Arnau

Maso²,

Lliso³

et al. 2009

TOMIJ

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“…The initial radii a_il, a_i2, a_i3 along the main axes are found as a_ij = 2. X/~, Aj being the eigenvalue corresponding to eigenvector j [1]. The initial rotation R_i~, R_iu, R_iz is also derived from the directions of the eigenvectors.…”

Section: Fitting a Superquadric To Voxel Datamentioning

confidence: 99%

“…We employ a voting-based test that analyzes the curve of Euclidean distances between superquadric paths over time. The value of the distance curve di,j(t) between the paths of two superquadrics Qi E Q (1) and Qj E Q(1) at time t is defined as the Euclidean distance between their respective positions on the paths at t. In order to decide if Qi and Qj lie on the same rigid body we look for the presence of two features in the distance curves.…”

Section: Body Part Identificationmentioning

confidence: 99%

Marker-free kinematic skeleton estimation from sequences of volume data

Theobalt

Aguiar

Magnor

et al. 2004

Proceedings of the ACM Symposium on Virtual Reality Software and Technology

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For realistic animation of an artificial character a body model that represents the character's kinematic structure is required. Hierarchical skeleton models are widely used which represent bodies as chains of bones with interconnecting joints. In video motion capture, animation parameters are derived from the performance of a subject in the real world. For this acquisition procedure too, a kinematic body model is required. Typically, the generation of such a model for tracking and animation is, at best, a semi-automatic process. We present a novel approach that estimates a hierarchical skeleton model of an arbitrary moving subject from sequences of voxel data that were reconstructed from multiview video footage. Our method does not require a-priori information about the body structure. We demonstrate its performance using synthetic and real data.

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“…[129]. Multiscale geometric representations have recently been used in computer graphics to model the complex geometry of biological organs in a hierarchical manner [7]. These methods are currently being investigated to introduce multiscale geometric representations of tree crowns to model radiative transfers in canopies.…”

Section: Multiscale Geometric Representationsmentioning

confidence: 99%

Representing and encoding plant architecture: A review

2000

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-A plant is made up of components of various types and shapes. The geometrical and topological organisation of these components defines the plant architecture. Before the early 1970's, botanical drawings were the only means to represent plant architecture. In the past two decades, high-performance computers have become available for plant growth analysis and simulation, triggering the development of various formal representations and notations of plant architecture (strings of characters, axial trees, tree graphs, multiscale graphs, linked lists of records, object-oriented representations, matrices, fractals, sets of digitised points, etc.). In this paper, we review the main representations of plant architecture and make explicit their common structure and discrepancies. The apparent heterogeneity of these representations makes it difficult to collect plant architecture information in a generic format to allow multiple uses. However, the collection of plant architecture data is an increasingly important issue, which is also particularly time-consuming. At the end of this review, we suggest that a task of primary importance for the plant-modelling community is to define common data formats and tools in order to create standard plant architecture database systems that may be shared by research teams. plant architecture / geometry / topology / scales of representation / encoding Résumé -Représentation et codage de l'architecture des plantes. Une plante est constituée d'entités ayant des types et des formes variés. L'organisation géométrique et topologique de ses entités définit « l'architecture de la plante ». Avant le début des années 70, la seule façon de représenter l'architecture des plantes était de faire des dessins botaniques précis. Dans les deux dernières décennies, l'utilisation d'ordinateurs de plus en plus puissants a permis de concevoir des modèles de simulation de croissance de plante capables de produire des architectures détaillées et de les visualiser. Ceci a favorisé l'émergence d'un ensemble varié de méthodes de repré-sentation de l'architecture des plantes (chaines de caractères, « axial trees », graphes arborescents, graphes multi-échelles, listes chaî-nées, représentations objet, matrices, fractales, ensemble de points digitalisés, etc.). Dans ce papier, nous passons en revue les principales représentations de l'architecture des plantes, en insistant sur leurs spécificités, mais aussi sur leurs points communs. L'hété-rogénéïté apparente de ces représentations rend la collecte des informations décrivant l'architecture des plantes difficilement réutili-sable. Toutefois, la mesure de « données architecturales » est un élément d'une importance capitale dans la conception de modèles structure/fonction. C'est aussi une tâche particulièrement longue et fastidieuse. C'est pourquoi nous suggérons à l'issue de cette revue, qu'une action de première importance à mener dans la communauté de modélisation est de définir des formats de données et des outils communs pour créer des bases de données ar...

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The ellipsoidal skeleton in medical applications

Cited by 15 publications

References 36 publications

Quantifying Uncertainties for Prostate Image-Guided Radiotherapy: A 3D Organ Reconstruction and Registration Method

Quantifying Uncertainties for Prostate Image-Guided Radiotherapy: A 3D Organ Reconstruction and Registration Method

Marker-free kinematic skeleton estimation from sequences of volume data

Representing and encoding plant architecture: A review

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