A morphing-Based 3D point cloud reconstruction framework for medical image processing. (September 2020)

• Record Type: Journal Article
• Authors: Cheng, Qiangqiang; Sun, Pengyu; Yang, Chunsheng; Yang, Yubin; Liu, Peter Xiaoping
• Is Part Of: Computer methods and programs in biomedicine. Volume 193(2020)
• Publication Date: 2020-09
• Subjects: Soft tissue -- Point cloud model -- Reconstruction -- Morphing -- Voxel -- Deformation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105495
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In this work, we proposed an innovative method to improve computational performance and vision effect of the 3D reconstruction point cloud model of soft tissue by applying Morphing. Due to the nonlinear interpolation method in Morphing, our proposed method fits well the irregular shape of model and enhances simulation accuracy. The reconstructed 3D point cloud model is applied to the simulation of soft tissue deformation in virtual surgery system, and good visual effects are validated. 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.