用混合弹性模型解决图象变形匹配问题

由于用传统的刚体匹配方法难以解决待匹配图象之间的结构差异 ,因此需要引入变形模型来进行图象的非刚体匹配 .为此提出了一种利用混合弹性模型 (HEM)来解决图象变形匹配问题的新方法 .该方法不需要预先提取图象的特征 ,而是直接利用匹配图象之间的灰度信息来实现图象之间的匹配 .匹配时 ,首先通过基于主轴的方法来实现两幅图象之间的全局仿射匹配 ;然后利用线性弹簧网模型来求取两幅图象之间的相关性 ,并进一步利用薄板样条模型来实现两幅图象的变形匹配 .该方法在匹配过程中还采取了多分辨率匹配策略 ,合成图象和医学脑图象的实验结果表明 ,该方法是有效的

Deformable Image Matching Using Hybrid Elastic Models

Comparing function or morphology between individuals requires non rigid matching, because the detail spatial structure difference between the image pair to be matched is too complicated to be modeled by any parameterized transformation. The goal of deformable matching method is to remove structural variation between the image pair to be matched. In this paper, a new method of deformable image matching based on hybrid elastic models (HEM) is proposed. The method, which need not extract features, works directly on grey level images. The algorithm first globally aligns images with a principal axis method, and then utilizes the linear spring net model for the correspondence and the thin plate spline for the non rigid mapping. This method takes multiresolution strategy to approach better matching. The elastic constant of the spring model will decreases as the process proceeds. Some experiments are performed on both synthetic and segmented medical images. It is shown that our hybrid elastic models can be successfully applied into the deformable image matching to remove the detail structural variation, and achieve good results.