Adaptive Meshing Based on the Multi-level Partition of Unity and Dynamic Particle Systems for Medical Image Datasets

International Journal Bioautomation

Quanquan Yang¹  Xiaobing Chen¹  Zhiwei Hou¹  Zhong Chen² 

[1] ;Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huaian, China;

关键词: Medical computed tomography;    Point clouds;    Multi-level partition of unity;    Dynamic particle systems;    Gaussian curvature;   

DOI  :  10.7546/ijba.2018.22.3.229-238

来源: DOAJ

【 摘 要 】

Surface meshes extracted from sparse medical images contain surface artifacts, there will produce serious distortion and generate numerous narrow triangle meshes. In order to eliminate the impact of the above factors, this paper presents a novel method for generating smooth and adaptive meshes from medical image datasets. Firstly, extracting the stack of contours by means of image segmentation and translating the contours into point clouds. The improved Multi-Level Partition of Unity (MPU) implicit functions are used to fit the point clouds for creating the implicit surface. Then, sampling implicit surface through dynamic particle systems based on Gaussian curvature, dense particles sampling in the high curvature region, sparse particles sampling in the low curvature region. Finally, generating triangle meshes based on particle distribution by using the Delaunay triangulation algorithm. Experimental results show that the proposed method can generate high-quality triangle meshes with distributed adaptively and have a nice gradation of triangle mesh density on the surface curvature.

【 授权许可】

Unknown