...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Multimodal image registration using floating regressors in the joint intensity scatter plot.
【24h】

Multimodal image registration using floating regressors in the joint intensity scatter plot.

机译：在联合强度散点图中使用浮动回归器进行多峰图像配准。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a new approach for multimodal medical image registration and compares it to normalized mutual information (NMI) and the correlation ratio (CR). Like NMI and CR, the new method's measure of registration quality is based on the distribution of points in the joint intensity scatter plot (JISP); compact clusters indicate good registration. This method iteratively fits the JISP clusters with regressors (in the form of points and line segments), and uses those regressors to efficiently compute the next motion increment. The result is a striking, dynamic process in which the regressors float around the JISP, tracking groups of points as they contract into tight clusters. One of the method's strengths is that it is intuitive and customizable, offering a multitude of ways to incorporate prior knowledge to guide the registration process. Moreover, the method is adaptive, and can adjust itself to fit data that does not quite match the prior model. Finally, the method is efficiently expandable to higher-dimensional scatter plots, avoiding the "curse of dimensionality" inherent in histogram-based registration methods such as MI and NMI. In two sets of experiments, a simple implementation of the new registration framework is shown to be comparable to (if not superior to) state-of-the-art implementations of NMI and CR in both accuracy and convergence robustness.
机译：本文提出了一种用于多模式医学图像配准的新方法，并将其与归一化互信息（NMI）和相关比（CR）进行了比较。与NMI和CR一样，新方法的注册质量度量基于联合强度散点图（JISP）中点的分布;紧密簇表明良好的配准。该方法迭代地将JISP集群与回归变量（以点和线段的形式）拟合，并使用这些回归变量来有效地计算下一个运动增量。结果是一个惊人的动态过程，在该过程中，回归器在JISP周围浮动，跟踪点组，当它们收缩成紧密的簇时。该方法的优势之一是直观且可自定义，它提供了多种方法来整合先验知识以指导注册过程。此外，该方法是自适应的，并且可以调整自身以适合与先前模型不太匹配的数据。最后，该方法可以有效地扩展到更高维的散点图，从而避免了基于直方图的配准方法（如MI和NMI）固有的“维数诅咒”。在两组实验中，新的注册框架的简单实现在准确性和收敛性方面都可以与NMI和CR的最新实现相媲美（如果不是更好的话）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号