Distance-Driven Skeletonization in Voxel Images

Arcelli, Carlo; Baja, Gabriella Sanniti di; Serino, Luca

doi:10.1109/tpami.2010.140

Cited by 96 publications

(116 citation statements)

References 48 publications

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“…In general, surface voxelization is addressed by spatial decomposition techniques such as kd-trees or octrees [5], [10], [11], from which a resulting volumetric representation enables fast access to point locations and to their corresponding neighbors, i.e., without re-computing distances between each other every time. However, more complex geometric processing, e.g., extracting 3-D object descriptors such as curve-skeletons [7], [12], or computing volumetric distance fields [6]; need to consider not only those voxels representing the external object surface but also those voxels considered to be interior to the object, i.e., solid voxelization. Voxelization is a necessary step in graphic computing for data simplification, visibility determination, or collision detection.…”

Section: Background and Related Workmentioning

confidence: 99%

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CPU-Based Real-Time Surface and Solid Voxelization for Incomplete Point Cloud

Garcia

Ottersten

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-This paper presents a surface and solid voxelization approach for incomplete point cloud datasets. Voxelization stands for a discrete approximation of 3-D objects into a volumetric representation, a process which is commonly employed in computer graphics and increasingly being used in computer vision. In contrast to surface voxelization, solid voxelization not only set those voxels related to the object surface but also those voxels considered to be inside the object. To that end, we first approximate the given point set, usually describing the external object surface, to an axis-aligned voxel grid. Then, we slice-wise construct a shell containing all surface voxels along each gridaxis pair. Finally, voxels inside the constructed shell are set. Solid voxelization results from the combination of all slices, resulting in a watertight and gap-free representation of the object. The experimental results show a high performance when voxelizing point cloud datasets, independently of the object's complexity, robust to noise, and handling large portions of data missing.

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Section: Background and Related Workmentioning

confidence: 99%

“…By doing so, further processing steps such as the computation of the volumetric distance field [6] or complex 3-D descriptors, i.e., curve-skeletons [7], are significantly This work was supported by the National Research Fund, Luxembourg, under the CORE project C11/BM/1204105/FAVE/Ottersten. simplified.…”

Section: Introductionmentioning

confidence: 99%

CPU-Based Real-Time Surface and Solid Voxelization for Incomplete Point Cloud

Garcia

Ottersten

2014

2014 22nd International Conference on Pattern Recognition

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“…The Chamfer distance transform is not always equivalent to the discrete morphology approach, unless the Chamfer ball is used as structuring element. Recent extensions of discrete distance transforms in 3D for skeletonization can be found in [2,6]. One main disadvantage of the skeleton is its sensitivity on perturbations of the boundary.…”

Section: Multiscale Skeletonization On Graphsmentioning

confidence: 99%

Segmentation and Skeletonization on Arbitrary Graphs Using Multiscale Morphology and Active Contours

Maragos

Drakopoulos

2012

Mathematics and Visualization

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In this chapter we focus on formulating and implementing on abstract domains such as arbitrary graphs popular methods and techniques developed for image analysis, in particular multiscale morphology and active contours. To this goal we extend existing work on graph morphology to multiscale dilation and erosion and implement them recursively using level sets of functions defined on the graph's nodes. We propose approximations to the calculation of the gradient and the divergence of vector functions defined on graphs and use these approximations to apply the technique of geodesic active contours for object detection on graphs via segmentation. Finally, using these novel ideas, we propose a method for multiscale shape skeletonization on arbitrary graphs.

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“…Skeletonization has been widely used in different medical imaging applications, including, thickness computation [2], topological classification [3,4], path finding [5], and object shape modeling [6]. Many 3D skeletonization algorithms [7][8][9][10][11] have been reported for binary digital objects. But the same is not true for fuzzy skeletonization.…”

Section: Introducationmentioning

confidence: 99%

A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging

Jin

Saha

2013

Image Analysis and Processing – ICIAP 2013

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Abstract. Skeletonization provides a simple yet compact representation of an object and is widely used in medical imaging applications including volumetric, structural, and topological analyses, object representation, stenoses detection, path-finding etc. Literature of three-dimensional skeletonization is quite matured for binary digital objects. However, the challenges of skeletonization for fuzzy objects are mostly unanswered. Here, a framework and an algorithm for fuzzy surface skeletonization are developed using a notion of fuzzy grassfire propagation which will minimize binarization related data loss. Several concepts including fuzzy axial voxels, local and global significance factors are introduced. A skeletal noise pruning algorithm using global significance factors as significance measures of individual branches is developed. Results of application of the algorithm on several medical objects have been illustrated. A quantitative comparison with an ideal skeleton has demonstrated that the algorithm can achieve sub-voxel accuracies at various levels of noise and downsampling. The role of fuzzy skeletonization in thickness computation at relatively low resolution has been demonstrated.

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Distance-Driven Skeletonization in Voxel Images

Cited by 96 publications

References 48 publications

CPU-Based Real-Time Surface and Solid Voxelization for Incomplete Point Cloud

CPU-Based Real-Time Surface and Solid Voxelization for Incomplete Point Cloud

Segmentation and Skeletonization on Arbitrary Graphs Using Multiscale Morphology and Active Contours

A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging

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