基于统计先验形状的水平集图像分割综述

Review of Statistical Shape Prior-based Level Set Image Segmentation

对于图像分割来说,常常需要结合尽可能多的先验信息来分割感兴趣组织。对基于统计先验形状的水平集图像分割方法进行了综述。该分割模型的特点是能量函数由两部分组成:首先是基于图像的梯度或区域灰度的数据项;第二项是先验形状项,对处理因遮挡、噪声和裂口而导致的信息缺失的图像具有鲁棒性。深入讨论了如何从感兴趣组织的训练集中构建一个压缩的形状表达——隐含形状模型;如何构建既包括使全局形状一致的隐含曲面约束,又保持了水平集捕捉局部形变的能力的基于先验形状的水平集图像分割模型;介绍了形状对齐和一致性等关键问题。最后指出了目前存在的问题和进一步的发展方向。

Image segmentation problem often demands the incorporation of as much prior information as possible to help the segmentation algorithms extract the tissue of interest. The model of image segmentation based on statistical shape prior level set was reviewed. The feature of mode is the energy function of the model composed by two terms. The first one is data term based on the image gradient or region gray intensity, the second one is the shape prior term which pro- vides robustness against missing shape information due to cluttering,occlusion and gaps. How to construct the implicit shape model which aims to extract a compact representation for the structure of interest from a set of training examples, how to construct the evolve model to constrain an implicit surface to follow global shape consistence while preser ving its ability to capture local deformation were discussed intensively. The key problems such as shape registration and the correspondence problem were introduced. Finally the open issues and possible future research directions were pointed.