Joint image segmentation and registration based on a dynamic level set approach using truncated hierarchical B-splines
Joint image segmentation and registration based on a dynamic level set approach using truncated hierarchical B-splines
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DOI：
10.1016/j.camwa.2019.04.026
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Aishwarya Pawar;Y. Zhang;C. Anitescu;T. Rabczuk
中科院分区：
文献类型：
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作者：
Aishwarya Pawar;Y. Zhang;C. Anitescu;T. Rabczuk
我们提出了一种新的方法，联合图像分割和非刚性配准使用双向组合水平集公式。这种有效的框架将自动结构分析从图像分割到配准框架。与分别进行分割和配准相比，该方法显示出改进的性能。与以前的方法不同，定义分割轮廓的隐式水平集函数和映射用于图像配准的变形的空间变换函数都使用B样条来定义。这种联合水平集框架使用了一种基于图谱的分割的变分形式，以及基于大变形的非刚性配准。此外，引入了一个双向组合框架，以包含更对称的更新。的变分形式的最小化是通过一组连续细化的自适应网格在多个图像分辨率的动态评价。使用高阶样条对分割结果的描述的改进导致图像分割和配准过程的更好的准确性。所提出的方法的性能证明合成和医学图像相比，其他配准方法的改进。
We present a novel approach for joint image segmentation and nonrigid registration using bidirectional composition based level set formulation. This efficient framework incorporates automatic structural analysis from image segmentation into the registration framework. This method has shown an improved performance as compared to carrying out segmentation and registration separately. Unlike previous approaches, the implicit level set function defining the segmentation contour and the spatial transformation function that maps the deformation for image registration are both defined using B-splines. This joint level set framework uses a variational form of an atlas-based segmentation together with large deformation based nonrigid registration. In addition, a bidirectional composition framework is introduced to incorporate a more symmetric update. The minimization of the variational form is accomplished by dynamic evaluations on a set of successively refined adaptive grids at multiple image resolutions. The improvement in the description of the segmentation result using higher order splines leads to a better accuracy of both the image segmentation and registration process. The performance of the proposed method is demonstrated on synthetic and medical images to show the improvement as compared to other registration methods.