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A statistical shape model using 2D-principal component analysis from few medical samples and its evaluation

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6 Author(s)
Tomoko Tateyama ; Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan ; Taishi Tanaka ; Shinya Kohara ; Amir Hossein Foruzan
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Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.

Published in:

Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on

Date of Conference:

23-25 June 2010