Roy Snehashis, Carass Aaron, Prince Jerry L
Image Analysis and Communications Laboratory, The Johns Hopkins University.
Proc SPIE Int Soc Opt Eng. 2013 Mar 12;8669. doi: 10.1117/12.2006682.
This paper presents a patch based method to normalize temporal intensities from longitudinal brain magnetic resonance (MR) images. Longitudinal intensity normalization is relevant for subsequent processing, such as segmentation, so that rates of change of tissue volumes, cortical thickness, or shapes of brain structures becomes stable and smooth over time. Instead of using intensities at each voxel, we use patches as image features as a patch encodes neighborhood information of the center voxel. Once all the time-points of a longitudinal dataset are registered, the longitudinal intensity change at each patch is assumed to follow an auto-regressive (AR(1)) process. An estimate of the normalized intensities of a patch at every time-point are generated from a hidden Markov model, where the hidden states are the unobserved normalized patches and the outputs are the observed patches. A validation study on a phantom dataset shows good segmentation overlap with the truth, and an experiment with real data shows more stable rates of change for tissue volumes with the temporal normalization than without.
本文提出了一种基于补丁的方法，用于对纵向脑磁共振（MR）图像的时间强度进行归一化处理。纵向强度归一化对于后续处理（如分割）至关重要，这样组织体积、皮质厚度或脑结构形状的变化率随时间会变得稳定且平滑。我们不是使用每个体素的强度，而是将补丁用作图像特征，因为补丁编码了中心体素的邻域信息。一旦纵向数据集的所有时间点都配准好了，每个补丁处的纵向强度变化假定遵循自回归（AR(1)）过程。从一个隐马尔可夫模型生成每个时间点补丁归一化强度的估计值，其中隐藏状态是未观察到的归一化补丁，输出是观察到的补丁。对一个体模数据集的验证研究表明与真实情况有良好的分割重叠，并且一项真实数据实验表明，与未进行时间归一化相比，进行时间归一化后组织体积的变化率更稳定。
