Nain Delphine, Haker Steven, Bobick Aaron, Tannenbaum Allen
College of Computing, Georgia Institute of Technology, Atlanta, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):66-74. doi: 10.1007/11866565_9.
This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.
本文提出了一种使用多尺度形状表示和先验知识的新型主动表面分割算法。我们使用球面小波函数定义表面的参数模型，并学习小波系数上的先验概率分布，以对训练集中不同尺度和空间位置的形状变化进行建模。基于这种表示，我们使用多尺度先验系数作为优化过程的参数，推导了一种参数化主动表面演化，以便在分割框架中自然地纳入先验知识。此外，优化方法可以以粗到精的方式应用。我们将算法应用于脑尾状核的分割，这在精神分裂症研究中具有重要意义。我们的验证表明，我们的算法计算效率高，并且通过捕捉更精细的形状细节，优于主动形状模型算法。
