A Parallel 3D 12-Subiteration Thinning Algorithm

Palágyi, Kálmán; Kuba, Attila

doi:10.1006/gmip.1999.0498

Cited by 175 publications

(127 citation statements)

References 24 publications

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“…In this paper, we utilize the skeleton descriptor to translate the shape of an iso-surface in the density volume into a graph structure that can be used to identify connectivity among helices. Such a skeleton can be efficiently generated from a discrete volume by iterative thinning [Bertrand 1995;Borgefors et al 1999;Palágyi and Kuba 1999;Svensson et al 2002;Ju et al 2006].…”

Section: Previous Workmentioning

confidence: 99%

Shape modeling and matching in identifying protein structure from low-resolution images

Abeysinghe

Chiu

et al. 2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

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Figure 1: Identifying α-helices in a low-resolution protein image, using the Human Insulin Receptor -Tyrosine Kinase Domain (1IRK) as an example. The inputs are the amino-acid sequence of the protein (a), where α-helices are highlighted in green, and a density volume reconstructed from electron cryomicroscopy (b), where possible locations of α-helices have been detected as cylinders shown in (c). Our method computes the correspondence between the helices in the sequence and in the density volume (e). This is achieved by extracting a skeleton from the density volume shown in (d) and matching it with the sequence in (a). Note that the matching is error-tolerant therefore the resulting correspondence does not have to be a bijection. AbstractIn this paper, we describe a novel, shape-modeling approach to recovering 3D protein structures from volumetric images. The input to our method is a sequence of α-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of α-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.

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Section: Previous Workmentioning

confidence: 99%

Shape modeling and matching in identifying protein structure from low-resolution images

Abeysinghe

Chiu

et al. 2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

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“…In 3D, objects can be i) directly reduced to their curve skeletons, e.g., [15,16], or ii) they can be first reduced to their surface skeletons, e.g., [17,18], which are then furthermore compressed to the curve skeletons, e.g., [19,20]. Since object recovery is possible only from the surface skeleton, we prefer algorithms of the latter type.…”

Section: Skeletonization In 3dmentioning

confidence: 99%

Skeletonization of Digital Objects

Baja

2006

Lecture Notes in Computer Science

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“…For example, two-voxel thick parts of the foreground can not be properly detected using a 3 × 3 × 3 neighbourhood. This can be solved based on the use of subiterations, where the foreground is eroded from one direction only in each subiteration; or a subfield sequential method, where the image is examined in a directional and sequential fashion [4]. Yet another approach is to use a recursive neighbourhood [17].…”

Section: Surface Skeletonizationmentioning

confidence: 99%

“…This property is useful if shape analysis related to changes in thickness of the object is performed. Various approaches to compute the surface skeleton of the foreground set in a 3D image can be found in literature, [1,2,3,4,5,6]. The interest of this paper deals with the latter two, [5,6].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Shape Primitives for Reversible Surface Skeletonization

Svensson

Jonker

2003

Discrete Geometry for Computer Imagery

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Abstract. We use a mathematical morphology approach to compute the surface and curve skeletons of a 3D object. We focus on the behaviour of the surface skeleton, in particular the reversibility for the case when the skeleton is, and is not anchored to the set of centres of maximal balls. We elaborate on the difficulties to obtain a reversible surface skeleton that does not depend on the orientation of the original object with respect to the grid, and that has no jagged borders.

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A Parallel 3D 12-Subiteration Thinning Algorithm

Cited by 175 publications

References 24 publications

Shape modeling and matching in identifying protein structure from low-resolution images

Shape modeling and matching in identifying protein structure from low-resolution images

Skeletonization of Digital Objects

On the Use of Shape Primitives for Reversible Surface Skeletonization

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