【摘要】 本文提出一种新的活动轮廓算法,即层次的上下文活动轮廓(HCAC),并将其应用于三维CT肝脏全自动分割。HCAC是一种基于机器学习的算法,可以分为两个阶段:第一阶段,即学习阶段,给定一套腹部三维CT训练图像以及对应的手动肝脏分割结果,利用上下文特征将每次的自动分割结果向手动参考分割结果映射,迭代学习得到一组纠错分类器;第二阶段,即分割阶段,首先将待分割图像用基本的活动轮廓进行分割,分割结果输入第一个纠错分类器,输出第一个形状模型,然后结合图像信息和当前形状模型,采用上下文活动轮廓(CAC)进行再一次分割,得到的分割结果输入第二个纠错分类器,输出第二个形状模型,结合图像信息和当前形状模型,再次采用CAC进行分割。如此迭代分割,随着形状模型的逐步精确,最终我们可以取得准确的肝脏分割。本文实验结果表明,随着迭代的深入,我们取得越来越好的分割结果。在三维CT肝脏图像分割中,我们通过6次迭代,就可以取得较好的肝脏分割。更多还原

【Abstract】 In this paper,we propose a new active contour algorithm,i.e.hierarchical contextual active contour(HCAC),and apply it to automatic liver segmentation from three-dimensional CT(3D-CT)images.HCAC is a learning-based method and can be divided into two stages.At the first stage,i.e.the training stage,given a set of abdominal 3D-CT training images and the corresponding manual liver labels,we tried to establish a mapping between automatic segmentations(in each round)and manual reference segmentations via context features,and obtained a series of self-correcting classifiers.At the second stage,i.e.the segmentation stage,we firstly used the basic active contour to segment the image and subsequently used the contextual active contour(CAC)iteratively,which combines the image information and the current shape model,to improve the segmentation result.The current shape model is produced by the corresponding self-correcting classifier(the input is the previous automatic segmentation result). The proposed method was evaluated on the datasets of MICCAI 2007liver segmentation challenge.The experimental results showed that we would get more and more accurate segmentation results by the iterative steps and the satisfied results would be obtained after about six rounds of iterations.更多还原