Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images
Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images
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基于图谱的骨闪烁扫描图像分割的变形场统计正则化
DOI：
10.1007/978-3-642-04268-3_82
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
L. Edenbrandt
中科院分区：
文献类型：
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作者：
K. Sjöstrand;M. Ohlsson;L. Edenbrandt
基于非刚性图像配准方法的变形统计模型的构建和应用近年来得到了广泛的关注。本文介绍了这样一个模型的应用，以限制一个通用的注册算法解剖合理的解决方案。具体而言，Morphon配准方法用于骨造影图像的基于图谱的分割。从734幅图像的训练集中，建立了特征变形场模型，并用于正则化113幅测试图像的配准。结果表明，大约300个训练图像和30个主模式足以建立一个有用的模型。113幅测试图像中有106幅分割成功。
The construction and application of statistical models of deformations based on non-rigid image registration methods have gained recent popularity. This paper presents the application of such a model to restricting a general-purpose registration algorithm to anatomically plausible solutions. Specifically, the Morphon registration method is used for atlas-based segmentation of bone scintigraphy images. From a training set of 734 images, a model of characteristic deformation fields is built and used for regularizing the registration of 113 test images. Results show that around 300 training images and 30 principal modes are sufficient for building a useful model. The segmentation succeeded in 106 of 113 test images.