Direct point-based registration for precise non-rigid surface matching using Student's-t mixture model. (March 2017)

• Record Type: Journal Article
• Authors: Zhou, Zhiyong; Tong, Baotong; Geng, Chen; Hu, Jisu; Zheng, Jian; Dai, Yakang
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 10
• Page End: 18
• Publication Date: 2017-03
• Subjects: Surface matching -- Point set registration -- Student's-t mixture model -- Dirichlet distribution; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.11.009
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A direct point set registration method based on Student's-t mixture model for precise non-rigid surface matching is proposed. Dirichlet distribution is introduced into the Student's-t mixture model for formulating the prior probabilities. Local spatial relationships between neighboring points are modeled by representing their posterior probabilities in a linear smoothing filter. Directional springs that are defined as the orientation changes of the undirected edges are introduced to preserve structures of surfaces. Abstract: One of the main challenges in the non-rigid surface matching is to match complex surfaces with absence of salient landmarks (marker-less) and salient structures (structure-less). We propose an accurate non-rigid surface registration method, called DSMM, to match complex surfaces based on a dense point-to-point correspondence alignment. The key idea of our approach is to model the correspondences on surfaces by using Student's-t mixture model and represent local spatial structures via Dirichlet distribution and the directional springs. Firstly, we formulate the problem of alignment of two point sets as a probability density estimate, modeling one set as Student's-t mixture model centroids, and the other one as observation data. We subsequently incorporate spatial representations of vertices on the surfaces into the prior probability of the finite Student's-t mixture model by exploiting the Dirichlet distribution and Dirichlet law. We later explicitly add an additional structure regularization to get an approximate isometric and near-conformal transformation. Finally, we obtain closed-form solutions of registration parameters using Expectation Maximization (EM) framework, leading to a computationally efficient registration method. We compare DSMM with other state-of-the-art direct point-based non-rigid surface matching methods based on finite mixture models on artificial shapes with large deformation and real complex shapes from various segmented brain structures. DSMM demonstrates its statistical accuracy and robustness, outperforming the competing