A 3D Matching Method for Organic Training Samples Alignment Based on Surface Curvature Distribution

Guangxu Li, Hyoungseop Kim, Joo Kooi Tan, Seiji Ishikawa, Akiyoshi Yamamoto

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DOI: 10.4236/ojmi.2011.12006                

Abstract

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 for organic training samples matching, whose modalities are orientable and surface figures could be recognized. It is a feature based alignment method which matches two models depending on the distribution of surface curvature. According to the affine transformation on 2D Gaussian map, the distances between the corresponding parts on surface could be minimized. We applied our proposed method on 5 cases left lung training samples alignment and 4 cases liver training samples alignment. The experiment results were performed on the left lung training samples and the liver training samples. The availability of proposed method was confirmed.

Keywords

Training Samples Alignment, Statistical Shape Model, Gauss Map, K-Means

Conflicts of Interest

The authors declare no conflicts of interest.

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