Segmentation priors from local image properties: Without using bias field correction, location-based templates, or registration
Segmentation priors from local image properties: Without using bias field correction, location-based templates, or registration
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DOI：
10.1016/j.neuroimage.2010.11.082
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发表时间：
2011-03-01
期刊：
影响因子：
5.7
通讯作者：
Saad, Ziad S.
中科院分区：
文献类型：
--
作者：
Vovk, Andrej;Cox, Robert W.;Saad, Ziad S.
我们提出了一种新的方法来生成关于一个体素的组织类成员基于其签名的信息-一个局部图像纹理的集合估计在一定范围内的邻域大小。该方法产生一种形式的组织类先验，可用于初始化和正则化图像分割。基于签名的方法与当前基于位置的方法不同，后者基于体素在标准模板空间中的位置派生组织类似然。为了使用基于位置的先验，需要将问题的体积注册到模板空间，并估计图像强度偏置场。当采用高阶非线性配准时，需要对一千多个参数进行两次优化。相比之下，基于签名的方法与体积方向、体素位置无关，并且对偏置场不敏感。由于这些原因，该方法不需要使用总体派生模板。先验信息由图像纹理统计的变化作为空间尺度的函数生成，并使用支持向量机方法将签名与组织类型关联起来。使用基于签名的方法，只需要在训练阶段优化SVM超平面的参数估计阶段，以及相关的pdf：一个独立于分割步骤的训练过程。我们发现基于签名的先验优于在有利条件下对齐的基于位置的先验，并且在FAST中取代基于位置的先验时，基于签名的先验可以改善分割（Zhang et al., 2001），这是一种广泛使用的分割程序。这项工作的软件实现可以作为AFNI http://afni.nimh.nih.gov的一部分免费获得。Elsevier Inc.出版。
We present a novel approach for generating information about a voxel's tissue class membership based on its signature-a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach is independent of volume orientation, voxel position, and largely insensitive to bias fields. For these reasons, the approach does not require the use of population derived templates. The prior information is generated from variations in image texture statistics as a function of spatial scale, and an SVM approach is used to associate signatures with tissue types. With the signature-based approach, optimization is needed only during the training phase for the parameter estimation stages of the SVM hyperplanes, and associated PDFs: a training process separate from the segmentation step. We found that signature-based priors were superior to location-based ones aligned under favorable conditions, and that signature-based priors result in improved segmentation when replacing location-based ones in FAST (Zhang et al., 2001), a widely used segmentation program. The software implementation of this work is freely available as part of AFNI http://afni.nimh.nih.gov. Published by Elsevier Inc.