Image Segmentation Based on Kernel PCA and Shape Prior

摘要：

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.

关键词： Deformable model level set principal component analysis shape prior

作者: Xiaoping WAN Djamal BOUKERROUI Jean-Pierre COCQUEREZ

作者单位: University of Technology of Compiegne, CNRS UMR 6599 Heudiasyc, BP 20529 - 60205 Compiegne Cedex, Fr University of Technology of Compiegne, CNRS UMR 6599 Heudiasyc, BP 20529 - 60205 Compiegne Cedex, Fr

会议类型: 国际会议

会议名称: Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)

会议地点: 成都

会议语种:英文

页码: 621-628

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）