A morphing-Based 3D point cloud reconstruction framework for medical image processing

Qiangqiang Cheng; Pengyu Sun; Chunsheng Yang; Yubin Yang; Peter Xiaoping Liu

doi:10.1016/j.cmpb.2020.105495

A morphing-Based 3D point cloud reconstruction framework for medical image processing

Comput Methods Programs Biomed. 2020 Sep:193:105495. doi: 10.1016/j.cmpb.2020.105495. Epub 2020 Apr 14.

Authors

Qiangqiang Cheng¹, Pengyu Sun², Chunsheng Yang³, Yubin Yang⁴, Peter Xiaoping Liu⁵

Affiliations

¹ the Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, China; the National Research Council, Ottawa, Canada. Electronic address: Qiangqiang.Cheng@nrc-cnrc.gc.ca.
² the Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, China. Electronic address: 1808085203022@stu.nchu.edu.cn.
³ the National Research Council, Ottawa, Canada. Electronic address: Yang@nrc-cnrc.gc.ca.
⁴ the State Key Laboratory for Novel Software Technology, Nanjing University, China. Electronic address: yangyubin@nju.edu.cn.
⁵ the Department of Systems and Computer Engineering, Carleton University, Ottawa, ON Canada. Electronic address: xpliu@sce.carleton.ca.

PMID: 32311509
DOI: 10.1016/j.cmpb.2020.105495

Abstract

Background and objective: In the virtual surgery simulation system, the reconstruction of a highly precise soft tissue 3D model is an effective method to improve the user's visual telepresence. However, the traditional point cloud generation method based on subdivision and filling is unsatisfactory due to its low accuracy and slow speed.

Methods: To address this problem, we present a novel 3D point cloud reconstructing model based on Morphing. The 3D surface model of soft tissue (live) is obtained from a series of 2D CT images using Mimics. The 3D voxel model of soft tissue is reconstructed through a sequential change of the 3D surface model by utilizing Morphing. A nonlinear interpolation method is used to fit the irregular shape of the model and improve simulation accuracy.

Results: The point cloud model builds from discrete points, avoiding the problems of instability and computational complexity, which are inherent in both the surface and volume models for soft tissue. Compared with the volumetric subdividing and voxel filling method, the simulation results show that the 3D cloud model reconstructed based on Morphing is more fast, accurate and consistent with the real soft tissue.

Conclusions: The simulating experiment of soft tissue deformation using 3D point cloud model which reconstructed using moprhing proved our method is effective and correct.

Keywords: Deformation; Morphing; Point cloud model; Reconstruction; Soft tissue; Voxel.

MeSH terms

Algorithms*
Computer Simulation
Image Processing, Computer-Assisted*
Imaging, Three-Dimensional