Ernst Moritz Arndt University of Greifswald, Institute for Community Medicine, Study of Health in Pomerania (SHIP), 17489 Greifswald, Germany.
Magn Reson Imaging. 2010 Jul;28(6):882-97. doi: 10.1016/j.mri.2010.03.010. Epub 2010 Apr 21.
Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.
自动 3D 肝脏磁共振（MR）数据集分割已被证明是医学图像分析领域中极具挑战性的任务。存在大量用于计算机断层扫描数据集的自动 3D 肝脏分割的方法，这些方法影响了 MR 图像的分割。与以前的 MR 数据集肝脏分割方法不同，我们使用不同权重的所有可用的 MR 通道信息，并在概率框架中形成肝脏组织和位置概率。我们应用多类线性判别分析作为一种快速有效的降维技术，并生成概率图，然后用于分割。我们开发了一种完全自动化的三步 3D 分割方法，基于修改后的区域生长方法和进一步的阈值技术。最后，我们结合特征先验知识来提高分割结果。这种新颖的 3D 分割方法是模块化的，可以应用于正常和脂肪积累的肝脏组织特性。
