Medical image segmentation by combining graph cuts and oriented active appearance models.
Medical image segmentation by combining graph cuts and oriented active appearance models.
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DOI：
10.1109/tip.2012.2186306
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发表时间：
2012-04
期刊：
影响因子：
--
通讯作者：
Yao J
中科院分区：
文献类型：
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作者：
Chen X;Udupa JK;Bagci U;Zhuge Y;Yao J
本文提出了一种基于活动外观模型(AAM)、带电导线(LW)和图割(GC)有效结合的三维分割方法。该方法包括三个主要部分：模型建立、初始化和分割。在建模部分，我们构造了AAM，训练了LW代价函数和GC参数。在初始化部分，提出了一种新的算法来改进传统的AAM匹配方法，将AAM和LW方法有效地结合起来，得到定向AAM(OAAM)。使用多对象策略来帮助对象初始化。我们采用伪3D初始化策略，并通过多目标OAAM方法逐层分割器官。对于分割部分，提出了一种基于三维形状约束的GC方法。将初始化步骤生成的对象形状整合到GC代价计算中，并使用迭代GC-OAAM方法进行对象划定。提出的方法在临床CT数据集上进行了肝脏、肾脏和脾的分割测试，并在MICCAI2007肝脏分割训练数据集上进行了测试。结果表明：(A)真阳性体积分数(TPVF)和假阳性体积分数(FPVF)的分割准确率分别为94.3%和0.2%。(B)将AAM和LW相结合可以提高初始化性能。(C)多对象策略极大地方便了初始化。(D)与传统的3DAAM方法相比，伪3DOAAM方法在性能相当的同时运行速度提高了12倍。(E)所提方法的性能可与最先进的肝脏分割算法相媲美。具有用户界面的3D形状约束GC的可执行版本可从网站http://xinjianchen.wordpress.com/research/.下载
In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) < 0.2% can be achieved. (b) The initialization performance can be improved by combining AAM and LW. (c) The multi-object strategy greatly facilitates the initialization. (d) Compared to the traditional 3D AAM method, the pseudo 3D OAAM method achieves comparable performance while running 12 times faster. (e) The performance of proposed method is comparable to the state of the art liver segmentation algorithm. The executable version of 3D shape constrained GC with user interface can be downloaded from website http://xinjianchen.wordpress.com/research/.