Two-stage multishape segmentation of brain structures using image intensity, tissue type, and location information
Two-stage multishape segmentation of brain structures using image intensity, tissue type, and location information
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DOI：
10.1118/1.3459018
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发表时间：
2010-08-01
期刊：
影响因子：
3.8
通讯作者：
Soltanian-Zadeh, Hamid
中科院分区：
文献类型：
--
作者：
Akhondi-Asl, Alireza;Soltanian-Zadeh, Hamid
目的：作者提出了一种快速，鲁棒，非参数，基于熵，耦合，多形状的方法来分割大脑皮层下结构的磁共振images.Methods：所提出的方法使用三种类型的信息：图像强度，组织类型和结构的位置。通过估计每个结构中的图像强度的概率密度函数（pdf）来捕获图像强度信息。通过将无监督组织分割方法应用于图像并估计每个结构的组织类型的概率质量函数（pmf）来捕获组织类型信息。通过从训练数据集中估计每个结构的位置的pdf来捕获位置信息。所得到的pmf和pdf用于定义熵函数，其最小值对应于结构的期望分割。作者提出了一个三步优化策略的分割方法。在第一步中，一个强大的自动初始化方法的基础上开发的组织类型和结构的位置信息。在第二步中，拟牛顿法被用来优化的能量函数的参数。为了加速迭代，能量函数相对于其参数的导数被解析地导出并用于优化过程中。在最后一步中，与先验形状模型相关的限制被删除，并应用水平集方法对分割结果进行微调。结果：将所提出的方法应用于两个不同的数据集，并将结果与文献中以前的方法进行了比较。实验结果是侧脑室，尾状核，丘脑，壳核，苍白球，海马，和amygdala.Conclusions：结果表明所提出的分割方法相比，其他方法在文献中的上级性能。该算法的执行时间为几分钟，适合各种应用。(C)2010年美国医学物理学家协会。[DOI：10.1118/1.3459018]
Purpose: The authors propose a fast, robust, nonparametric, entropy-based, coupled, multishape approach to segment subcortical brain structures from magnetic resonance images (MRIs).Methods: The proposed method uses three types of information: Image intensity, tissue types, and locations of structures. The image intensity information is captured by estimating the probability density function (pdf) of the image intensities in each structure. The tissue type information is captured by applying an unsupervised tissue segmentation method to the image and estimating a probability mass function (pmf) for the tissue type of each structure. The location information is captured by estimating pdf of the location of each structure from the training datasets. The resulting pmf's and pdf's are used to define an entropy function whose minimum corresponds to a desirable segmentation of the structures. The authors propose a three-step optimization strategy for the segmentation method. In the first step, a powerful automatic initialization method is developed based on tissue type and location information of the structures. In the second step, a quasi-Newton method is used to optimize the parameters of the energy function. To speed up the iterations, derivatives of the energy function with respect to its parameters are analytically derived and used in the optimization process. In the last step, the limitations related to the prior shape model are removed and a level-set method is applied for the fine tuning of the segmentation results.Results: The proposed method is applied to two different datasets and the results are compared to those of previous methods in literature. Experimental results are presented for lateral ventricles, caudate, thalamus, putamen, pallidum, hippocampus, and amygdala.Conclusions: The results illustrate superior performance of the proposed segmentation method compared to other methods in literature. The execution time of the algorithm is a few minutes, suitable for a variety of applications. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3459018]