Csapo Istvan, Davis Brad, Shi Yundi, Sanchez Mar, Styner Martin, Niethammer Marc
University of North Carolina at Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):280-8. doi: 10.1007/978-3-642-33454-2_35.
Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology. Image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, (i) local similarity measures, (ii) methods estimating intensity transformations between images, and (iii) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration in the presence of spatially non-uniform intensity change.
纵向成像研究经常被用于调查脑形态学的时间变化。图像强度也可能随时间变化，例如在研究大脑成熟过程时。然而，在标准图像配准方法的图像相似性度量中并未考虑这种强度变化。因此，（i）局部相似性度量、（ii）估计图像间强度变换的方法以及（iii）变形方法已被开发出来，以实现对强度变化的鲁棒性或同时捕捉空间和强度变化。对于这些方法，纵向强度变化没有被明确建模，并且图像被视为独立的静态样本。在此，我们提出一种基于模型的图像相似性度量，用于在存在空间不均匀强度变化的情况下进行纵向图像配准。
