Tunneling descent level set segmentation of ultrasound images.
Tunneling descent level set segmentation of ultrasound images.
复制标题
超声图像的隧道下降水平集分割。
DOI：
10.1007/11505730_62
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Tagare,HemantD
中科院分区：
文献类型：
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作者：
Tao,Zhong;Tagare,HemantD
由于超声图像中斑点的存在，使得活动轮廓的能量函数出现许多虚假的局部极小值。这些极小点在梯度下降下过早地陷入分割，导致算法失败。本文提出了一种新的隧道下沉算法，它是一种摆脱无用局部极小值的确定性技术。该算法将进化曲线用水平集表示，进化策略由约束极小化序列表示，并用该算法对115幅短轴心脏超声图像中的心内膜进行分割。在不调整能量函数或数值参数的情况下，实现了所有分割。实验结果表明，该算法克服了多个局部极小值，得到了比传统方法高得多的分割精度。
The presence of speckle in ultrasound images causes many spurious local minima in the energy function of active contours. These minima trap the segmentation prematurely under gradient descent and cause the algorithm to fail. This paper presents a substantially new reformulation of Tunneling Descent, which is adeterministictechnique to escape from unwanted local minima. In the new formulation, the evolving curve is represented by level sets, and the evolution strategy is obtained as a sequence of constrained minimizations.The algorithm is used to segment the endocardium in 115 short axis cardiac ultrasound images. All segmentations are achieved without tweaking the energy function or numerical parameters. Experimental evaluation of the results shows that the algorithm overcomes multiple local minima to give segmentations that are considerably more accurate than conventional techniques.