A surface mapping based alignment method for Statistical Shape Model building

The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method using surface parameterization theory to solve the rotation transformation of 3D rigid registration. It is a feature based alignment method which matches two models depending on comparing the distribution of spherical conformal map of vertices. Moreover, the stereographic projection is utilized to transform the spherical statistics to bifacial plane. The optimal solution is obtained by an iterated algorithm. We tested the rigid registration of left lung training samples. The availability of our proposed method was confirmed.