The introduction of shape priori in the segmentation model ameliorates effectively the poor segmentation result due to
the using of the image information alone to segment the image including noise, occlusion, or missing parts. But the
presentation of shape via Principal Component Analysis (PCA) brings on the limitation of the similarity between the
objet and the prior shape. In this paper, we proposed using Kernel PCA (KPCA) to capture the shape information - the
variability. KPCA can present better shape prior knowledge. The model based on KPCA allows segmenting the object
with nonlinear transformation or a quite difference with the priori shape. Moreover, since the shape model is
incorporated into the deformable model, our segmentation model includes the image term and the shape term to balance
the influence of the global image information and the shape prior knowledge in proceed of segmentation. Our model and
the model based on PCA both are applied to synthetic images and CT medical images. The comparative results show that
KPCA can more accurately identify the object with large deformation or from the noised seriously background.
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