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首页> 外文期刊>Journal of visual communication & image representation >Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer
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Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer

机译：多特征梯度向量流蛇用于乳腺癌超声图像的自适应分割

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Segmentation of ultrasound (US) images of breast cancer is one of the most challenging problems of the modern medical image processing. A number of popular codes for US segmentation are based on a generalized gradient vector flow (GGVF) method proposed by Xu and Prince. The GGVF equations include a smoothing term (diffusion) applied to regions of small gradients of the edge map and a stopping term to fix and extend large gradients appearing at the boundary of the object. The paper proposes two new directions. The first component is diffusion as a polynomial function of the intensity of the edge map. The second component is the orientation score of the vector field. The new features are integrated into the GGVF equations in the smoothing and the stopping term. The algorithms, having been tested by a set of ground truth images, show that the proposed techniques lead to a better convergence and better segmentation accuracy with the reference to conventional GGVF snakes. The adaptive multi-feature snake does not require any hand-tuning. However, it is as efficient as the standard GGVF with the parameters selected by the "brutal force approach". Finally, proposed approach has been tested against recent modifications of GGVF, i.e. the Poisson gradient vector flow, the mixed noise vector flow and the convolution vector flow. The numerical tests employing 195 synthetic and 48 real ultrasound images show a tangible improvement in the accuracy of segmentation.
机译：乳腺癌的超声（US）图像分割是现代医学图像处理中最具挑战性的问题之一。许多用于US分割的流行代码都是基于Xu和Prince提出的广义梯度向量流（GGVF）方法。 GGVF方程包括应用于边缘图的小梯度区域的平滑项（扩散）和用于固定和扩展出现在对象边界处的大梯度的终止项。本文提出了两个新的方向。第一个分量是扩散，它是边缘图强度的多项式函数。第二部分是矢量场的方向分数。在平滑和停止项中，新功能已集成到GGVF方程中。该算法已通过一组地面真实图像进行了测试，结果表明，相对于常规GGVF蛇，所提出的技术可带来更好的收敛性和更好的分割精度。自适应多功能蛇不需要任何手动调整。但是，它具有通过“蛮力法”选择的参数的标准GGVF的效率。最后，针对GGVF的最新修改（即泊松梯度向量流，混合噪声向量流和卷积向量流）对提出的方法进行了测试。使用195张合成超声图像和48张真实超声图像进行的数值测试显示了分割精度的明显提高。

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