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Improved Algorithm for Gradient Vector Flow Based Active Contour Model Using Global and Local Information

机译:全局和局部信息的基于梯度矢量流的主动轮廓模型改进算法

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摘要

Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.
机译:主动轮廓模型用于从数字图像中提取对象边界,但是对于具有深凹度的目标,收敛性较差。我们提出了一种基于现有梯度矢量流方法的改进方法。本文的主要贡献是从梯度矢量流的整体力中以更高的精度确定活动轮廓假部分的新算法,以及更新外力场和静磁力局部信息的新算法。我们的方法具有半动态外力场,仅当存在错误的活动轮廓时才对其进行调整。因此,主动轮廓有更多机会近似复杂边界,而有效地限制了计算成本。新算法将在不规则形状上进行测试,然后再在MRI和超声医学数据等真实图像上进行测试。实验结果说明了该方法的有效性,并分析了计算复杂度。

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