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.
Highlights
Due to utilizing the priori information of shapes, Statistical Shape Model (SSM) method shows concise and robust for segmentation, analyzing and interpreting anatomical objects from medical datasets [1]
In point sets based alignment method, the set of identified points is sparse compared with the original image content, which makes for relatively fast optimization procedures, but is difficult to confirm the corresponding points
We proposed a feature based alignment method to match a set of training shapes for SSM model building
Summary
Due to utilizing the priori information of shapes, Statistical Shape Model (SSM) method shows concise and robust for segmentation, analyzing and interpreting anatomical objects from medical datasets [1]. The basic idea in model building is to statistic the pattern of legal variation in the shapes and spatial relationships of structures from a collection of training samples. A key step in building a model involves establishing a dense correspondence between shape boundaries and surface figures. The training shapes alignment problems can be concluded that the transformation between the same anatomy images at different modalities or represented by one modality at different time [3]. Some novel strategies are reported to optimize measures such as the average distance between each representative point, or iterated minimal distances metric [4]. These methods are mostly used to find rigid or affine transformations. Because generally the landmarks positions always involve the local features of surface
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