Longitudinal Intensity Normalization of Magnetic Resonance Images using Patches.
Longitudinal Intensity Normalization of Magnetic Resonance Images using Patches.
复制标题
使用补丁对磁共振图像进行纵向强度标准化。
DOI：
10.1117/12.2006682
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Prince,JerryL
中科院分区：
文献类型：
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作者：
Roy,Snehashis;Carass,Aaron;Prince,JerryL
本文提出了一种基于补丁的方法，用于标准化纵向脑磁共振 (MR) 图像的时间强度。纵向强度标准化与分割等后续处理相关，以便组织体积、皮质厚度或大脑结构形状的变化率随着时间的推移变得稳定和平滑。我们不使用每个体素的强度，而是使用补丁作为图像特征，因为补丁编码中心体素的邻域信息。一旦记录了纵向数据集的所有时间点，则假设每个斑块的纵向强度变化遵循自回归（AR（1））过程。每个时间点的补丁的归一化强度的估计是从隐马尔可夫模型生成的，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。对虚拟数据集的验证研究表明，分割与真实情况有良好的重叠，而对真实数据的实验表明，经过时间标准化的组织体积的变化率比没有经过时间标准化的组织体积的变化率更稳定。
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.