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Active Contour Model Based on Local and Global Image Information

机译:基于局部和全局图像信息的主动轮廓模型

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In this paper, a new hybrid active contour model for image segmentation was proposed. In our model, we define a new region-based signed pressure force (SPF) function which combines the local and the global image information. It can stop the contour at the weak or blurred edges. The proposed model can not only detect the boundaries automatically, but also is robust to noise and not sensitive to the initialization of the contour. In addition, the model can segment all objects in image domain. Compared with the other active contour model, the proposed model can be simply implemented by the finite difference scheme. Experiments on the synthetic and medical images demonstrate that the proposed active contour model can efficiently segment the objects well.
机译:本文提出了一种新的混合主动轮廓模型用于图像分割。在我们的模型中,我们定义了一个新的基于区域的有符号压力(SPF)函数,该函数结合了局部和全局图像信息。它可以使轮廓停止在较弱或模糊的边缘。所提出的模型不仅可以自动检测边界,而且对噪声具有鲁棒性,并且对轮廓的初始化不敏感。此外,该模型可以分割图像域中的所有对象。与其他主动轮廓模型相比,所提出的模型可以通过有限差分方案简单地实现。在合成图像和医学图像上进行的实验表明,所提出的主动轮廓模型可以有效地分割对象。

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