Shape reconstruction, shape manipulation, and direct generation of input data from point clouds for rapid prototyping

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This paper presents a simple and effective method for reconstructing 3D bio-CAD models from a sequence of computer tomography (CT) medical image data. In this work, a heterogeneous implicit solid representation method is proposed to describe a heterogeneous model of the human body. The use of implicit solid as a basis for 3D bio-CAD models facilitates the robustness and simplification of the process for converting CT images to a 3D bio-CAD model. An almost perfect 3D bio-CAD model is achieved from implicit solid interpolation combined with domain decomposition. The implicit solid is defined by a radial basis function which is a continuous scalar-value function over the domain R 3 . The generated solid consists of the set of all points at which this scalar function value is the Houndsfield unit (HU) value obtained from the CT image. CT images of human bone were used as the case studies for the reconstruction of the 3D solid model from CT scan data. Experimental results show that the proposed method has the potential to represent internal human body details accurately and efficiently suitable for rapid prototyping, finite element analysis, and tissue engineering.

Section: Application Results and Discussionmentioning

confidence: 99%

Three-dimensional human body model reconstruction and manufacturing from CT medical image data using a heterogeneous implicit solid based approach

Yoo

2011

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“…Additional details concerning the offsetting procedure can be found in the author's previous works. 11,12 If we change the constant C in Eq.…”

Section: Thickened Solid Generationmentioning

confidence: 99%

Computer-aided porous scaffold design for tissue engineering using triply periodic minimal surfaces

Yoo

2011

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203

Recently, computer aided geometric design of triply periodic minimal surfaces (TPMS) has received considerable attention in the area of computer aided nano design on account of its ability to efficiently construct a large number of complex surfaces. In this paper, a TPMS is described with periodic surfaces composed of simple trigonometric functions, thus enabling easy generation of TPMS for use in various mechanical, chemical, and physical applications. We first describe a TPMS with mesh surface using conventional marching cube algorithm. We then propose various related algorithms for generating complete solid model for various applications, ranging from thickened solid, through voxel solid, to complexshaped solid. The validity of this new technique is demonstrated for a variety of TPMSs, including the P, G, D, I-WP, F-RD, L, and I 2 -Y** surfaces. Finally, a new control approach for pore size distribution in tissue scaffold design is presented based on the pore-making element composed of TPMS and conformal refinement of all-hexahedral mesh in order to show the practical applicability of the newly suggested modeling approach.

“…Thus, constructing a pointsampled model also is a very difficult task. Although many efforts have to be carried out by many researchers, [28][29][30][31] including the point cloud simplicity, feature extraction, and others, but great error usually is generated in those processes. Therefore, up to now, great development still has not been achieved.…”

Section: Application Examplesmentioning

confidence: 99%

A robust and efficient method for direct projection on point-sampled surfaces

Zhang

2010

A novel and robust approach for computing the direct projection onto a point cloud for a given 3D point with associated projection vector is proposed. An energy function is presented to model the active contour, and the curve evolving method is applied to track the projection in certain iterations. In each step of the iteration, the energies of the related points are used to estimate the step length and orientation of the evolution. Then the point's position is continuously updated and approximates the solution until the given accuracy is reached. The proposed approach directly operates on the point cloud, which is followed by a refining process to correct the results for under-sampled regions. Since the algorithm is carried out on a small point set (i.e., the neighborhood of the test point, generally composed of 30~60 points) it is not expensive computationally. By numerical experiments, the method has been demonstrated to be more robust and efficient than the existing direct projection approaches on processing discrete point clouds.