Roy Snehashis, Carass Aaron, Prince Jerry L
Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University.
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:342-345. doi: 10.1109/ISBI.2013.6556482.
Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.
磁共振（MR）成像（MRI）被广泛用于研究人类大脑的结构。与计算机断层扫描（CT）不同，MR图像强度没有特定组织的解释。因此，在不同扫描仪上使用相同成像序列或不同参数获得的同一受试者的图像具有广泛不同的强度尺度。这种不一致性在分割和其他图像处理任务中引入了误差，因此需要进行图像强度标准化。与以前使用直方图变换的强度归一化方法相比——这些方法试图基于直方图找到全局一对一的强度映射——我们提出了一种基于补丁的生成模型，用于在不同扫描仪或不同脉冲序列参数下采集的图像之间进行强度归一化。在对用各种参数模拟的体模进行归一化时，我们的方法优于基于直方图的方法。此外，在各种扫描仪和采集参数下获取的真实数据实验表明，经过我们的归一化后，分割结果更加一致。
