A digital database of wrist bone anatomy and carpal kinematics

Moore, Douglas C.; Crisco, Joseph J.; Trafton, Theodore G.; Leventhal, Evan L.

doi:10.1016/j.jbiomech.2006.10.041

Cited by 59 publications

(67 citation statements)

References 25 publications

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“…13 Briefly, bone surface models were created from CT scans (resolution 0.3 ϫ 0.3 mm, 1-mm slice thickness) of the wrists in a neutral position. For each scan, the image was thresholded and processed to yield a 3-dimensional cloud of points on the outer cortical surfaces that were tessellated to form a triangular surface (Geomagic Studio; Geomagic, Inc., Triangle Park, NC).…”

Section: Methodsmentioning

confidence: 99%

A Computational Approach to the “Optimal” Screw Axis Location and Orientation in the Scaphoid Bone

Leventhal

Wolfe

Walsh

et al. 2009

The Journal of Hand Surgery

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Section: Methodsmentioning

confidence: 99%

A Computational Approach to the “Optimal” Screw Axis Location and Orientation in the Scaphoid Bone

Leventhal

Wolfe

Walsh

et al. 2009

The Journal of Hand Surgery

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“…We perform an initial experiment on a human wrist bone dataset (courtesy of Dr. Crisco, RI Hospital [17]) to examine the shape variations of the carpal bones across patients. In Fig.6, the left-hand hamates of 6 females are matched against each other.…”

Section: Resultsmentioning

confidence: 99%

“…The MA is promising for shape recognition [19,21] in that (i) it organizes the shape information in a hierarchical, intrinsic graph-like structure [16], which enables matching parts of deformed shapes naturally, Figure 1. The matching of the MS hypergraphs of two carpal bones [17] in (a) and (b) is shown in (c). (d,e) show a manual correspondence, where the graph components are labeled in numbers, serving as the ground truth to validate the automatic matching.…”

Section: Introductionmentioning

confidence: 99%

Measuring 3D shape similarity by matching the medial scaffolds

Chang

Kimia

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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We propose to measure 3D shape similarity by matching a medial axis (MA) based representation-the medial scaffold (MS). Shape similarity is measured as the minimum extent of deformation necessary for one shape to match another, guided by the MS. This approach is an extension of an approach to match 2D shapes by matching their shock graphs, whereas here in 3D the MS is in the form of a hypergraph. The MS representation is both hierarchical and complete. Our approach finds the optimal deformation path between two shapes by modelling shape deformations as discrete topological changes (transitions) of the MS, with costs associated with each transition. We first regularize the MS hypergraphs and use the graduated assignment graph matching scheme to match the hypergraphs. A set of compatibility functions is defined to measure the pairwise similarity between the MS nodes, curves, and sheets. Early results on matching carpal bones and other shapes promise its potential in a range of applications.

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“…As there is a close geometric functional match between articulating surfaces and ligament geometry in an individual joint, each of these models need to be fed with the required geometric data of an individual joint. There are advanced studies available in literature which describe the anatomy of wrist joint components in detail (Nanno et al, 2006;Berger, 1997;Moore et al, 2007). However, the 3D geometry of both cartilage and ligaments from the same anatomical specimen is not available yet.…”

Section: Introductionmentioning

confidence: 99%