Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF > 96%, FPVF < 0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.

中文翻译：

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GC-ASM：用于医学图像分割的图割和主动形状模型策略的协同集成。


图像分割方法可以分为两类：纯粹基于图像的和基于模型的。这两个类别都有自己的优点和缺点。在本文中，我们提出了一种基于图像的图割（GC）方法与基于模型的ASM方法的新颖协同组合，以得到用于医学图像分割的GC-ASM方法。提出了一种多对象GC成本函数，将ASM形状信息有效地集成到GC框架中。所提出的方法包括两个阶段：模型构建和分割。在模型构建阶段，构建ASM模型并估计GC的参数。分割阶段包括两个主要步骤：初始化（识别）和描绘。对于初始化，提出了一种自动方法来估计模型的姿态（平移、方向和尺度），并获得粗略的分割结果，该结果也为GC方法提供形状信息。为了进行描绘，提出了一种迭代 GC-ASM 算法，该算法根据初始化结果执行更精细的描绘。所提出的方法可在 2D 图像上进行操作，并在临床胸部 CT、腹部 CT 和足部 MRI 数据集上进行评估。结果表明：（a）对于不同的物体、模态和身体区域，通过 GC-ASM 可以实现 TPVF > 96%、FPVF < 0.6% 的总体勾画精度。 (b) GC-ASM 在搜索区域的准确性和精确度方面比 ASM 有所改进。 (c) GC-ASM 需要的地标比 ASM 少得多（大约是 ASM 的 1/3）。 (d) 与需要种子规范的 GC 相比，GC-ASM 在分割步骤中实现了完全自动化，并提高了 GC 的准确性。 (e) GC-ASM 的一个缺点是由于算法的迭代性质而增加了计算费用。