基于联合聚类和配准算法的可形变数字化概率图谱研究

The Study of Deformable Digital Probabilistic Atlas Based on the Joint Clustering and Matching Algorithm

可形变形状模型化的一个关键问题就是从一系列未标记的形状点集中估计一个有意义的平均形状。本文给出一种新的联合聚类和配准算法,它可以从多个形状采样的未标记点集中计算出这样一个平均形状,利用一种交替更新方法,将这些形状采样点集非刚性匹配到正在形成的平均形状上,然后根据得到的非刚性变换来估计平均形状,整个过程是完全对称的。这种方法对医学图像处理中建立基于人群的概率图谱非常有用。实验中将其应用到4个不同人脑部2D胼胝体数据中建立图谱(平均形状)。

One of the key challenges in deformable shape modeling is to estimate a meaningful average or mean shape from a set of unlabeled shapes. We present a new joint clustering and matching algorithm that is capable of computing such a mean shape from multiple shape samples which are represented by unlabeled point-sets. An iterative bootstrap process is used wherein multiple shape sample point-sets are nonrigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these nonrigid alignments. The process is entirely symmetric. It is believed that this method can be especially useful for creating probabilistic atlas based on sub-population in medical image. And the method is applied to create mean shapes from four segmented 2D corpus callosum data sets.