Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Biomed Eng. 2010 Oct;57(10):2622-6. doi: 10.1109/TBME.2010.2056369. Epub 2010 Jul 8.
In this letter, we present an approach for automatic liver segmentation from computed tomography (CT) scans that is based on a statistical shape model (SSM) integrated with an optimal-surface-detection strategy. The proposed method is a hybrid method that combines three steps. First, we use localization of the average liver shape model in a test CT volume via 3-D generalized Hough transform. Second, we use subspace initialization of the SSM through intensity and gradient profile. Third, we deform the shape model to adapt to liver contour through an optimal-surface-detection approach based on graph theory. The proposed method is evaluated on MICCAI 2007 liver-segmentation challenge datasets. The experiment results demonstrate availability of the proposed method.
在这封信中，我们提出了一种基于统计形状模型（SSM）与最佳表面检测策略相结合的自动 CT 扫描肝脏分割方法。所提出的方法是一种混合方法，它结合了三个步骤。首先，我们使用三维广义霍夫变换在测试 CT 体积中定位平均肝脏形状模型。其次，我们通过强度和梯度轮廓进行 SSM 的子空间初始化。最后，我们通过基于图论的最佳表面检测方法来变形形状模型以适应肝脏轮廓。该方法在 MICCAI 2007 肝脏分割挑战数据集上进行了评估。实验结果表明了该方法的有效性。
