GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.
GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.
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DOI：
10.1016/j.cviu.2012.12.001
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发表时间：
2013-05
影响因子：
4.5
通讯作者：
Torigian, Drew A.
中科院分区：
文献类型：
--
作者：
Chen, Xinjian;Udupa, Jayaram K.;Alavi, Abass;Torigian, Drew A.
图像分割方法可以分为两类：纯图像分割和基于模型分割。这两类人各有利弊。本文将基于图像的图割(GC)方法与基于模型的ASM方法相结合，提出了一种新的用于医学图像分割的GC-ASM方法。提出了一种多目标GC代价函数，将ASM形状信息有效地集成到GC框架中。该方法分为两个阶段：模型建立和分割。在建模阶段，建立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)和描述。对于初始化，提出了一种自动估计模型姿态(平移、方向和比例)的方法，得到了大致的分割结果，并为GC方法提供了形状信息。在轮廓提取方面，提出了一种迭代GC-ASM算法，该算法基于初始化结果进行更精细的轮廓提取。将所提出的方法应用于二维图像上，并在临床胸部CT、腹部CT和足部MRI数据集上进行了评估。结果表明：(A)对于不同的物体、形态和身体区域，通过GC-ASM可以达到TPVF>；96%，FPVF<；0.6%的总体勾画准确率。(2)GC-ASM在搜索区域的准确度和精度上均优于ASM。(C)GC-ASM需要的地标比ASM少得多(约为ASM的三分之一)。(D)与要求种子规格的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开销。
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.
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影响因子：
3.8
作者：
Chen, Xinjian;Udupa, Jayaram K.;Torigian, Drew A.
通讯作者：
Torigian, Drew A.
影响因子：
2
作者：
Ciesielski, Krzysztof Chris;Chen, Xinjian;Grevera, George J.
通讯作者：
Grevera, George J.
DOI：
10.1006/gmip.1998.0475
发表时间：
1998-07-01
期刊：
GRAPHICAL MODELS AND IMAGE PROCESSING
影响因子：
--
作者：
Falcao, AX;Udupa, JK;Lotufo, RDA
通讯作者：
Lotufo, RDA
影响因子：
3.5
作者：
CHRISTENSEN, GE;RABBITT, RD;MILLER, MI
通讯作者：
MILLER, MI
DOI：
10.1109/34.244675
发表时间：
1993-11-01
影响因子：
23.6
作者：
COHEN, LD;COHEN, I
通讯作者：
COHEN, I