Effect of Number of Coupled Structures on the Segmentation of Brain Structures
Effect of Number of Coupled Structures on the Segmentation of Brain Structures
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DOI：
10.1007/s11265-008-0196-4
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发表时间：
2009-03-01
影响因子：
1.8
通讯作者：
Soltanian-Zadeh, Hamid
中科院分区：
文献类型：
--
作者：
Akhondi-Asl, Alireza;Soltanian-Zadeh, Hamid
本文报道了耦合信息对基于模型的磁共振图像脑结构分割性能的影响。我们开发了一种三维的、非参数的、基于熵的、多形状的方法，该方法受益于形状的耦合。该方法使用主成分分析(PCA)建立形状模型，该模型捕捉结构的变异性，并通过耦合不同结构之间的几何关系(限制它们的独立变形)来将它们集成到算法中。同时，为了允许耦合结构的变化，在建立形状模型时，它单独注册每个结构。定义了一个基于熵的能量函数，并用拟牛顿算法对其进行极小化。概率密度函数(Pdf)用非参数Parzen窗方法迭代估计。在优化算法中，为了获得最大的速度和精度，使用了解析导数。对于尾状核、丘脑、壳核、苍白球、海马体和杏仁核的分割，给出了样本结果，说明了与文献中最相似的方法相比，所提出的方法具有更好的性能。与最相似的分割方法相比，该方法得到的分割结果与专家分割的相似度提高了4%~12%。实验研究表明，该方法通过调节分割过程中的形状变化，使分割结果的准确率提高了6%，而另一种方法的精度提高了1%。此外，在耦合过程中使用的结构越多，得到的结果越准确。
This paper reports the effect of the coupling information on the performance of model-based segmentation of the brain structures from magnetic resonance images (MRI). We have developed a three-dimensional, nonparametric, entropy-based, and multi-shape method that benefits from coupling of the shapes. The proposed method uses principal component analysis (PCA) to develop shape models that capture structural variability and integrates geometrical relationship among different structures into the algorithm by coupling them (limiting their independent deformations). At the same time, to allow variations of the coupled structures, it registers each structure individually when building the shape models. It defines an entropy-based energy function which is minimized using quasi-Newton algorithm. Probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, analytical derivatives are used for maximum speed and accuracy. Sample results are given for the segmentation of caudate, thalamus, putamen, pallidum, hippocampus, and amygdala illustrating superior performance of the proposed method compared to the most similar method in the literature. The similarity of the results obtained by the proposed method with the expert segmentation is 4% to 12% higher than that of the most similar method. Experimental studies show that the proposed coupling method, which regulates shape variability during segmentation, improves accuracy of the results of the proposed method by 6% and those of the other method by 1%. In addition, the more the structures are used in the coupling process, the more accurate the results are obtained.