The conventional distance regularized level set evolution method has been very popular in image segmentation, but usually it cannot converge to the desired boundary when there are multiple and unwanted boundaries in the image. By observation, the gradient direction between the target boundaries and the unwanted boundaries are usually different in one image. The gradient direction information of the boundaries can guide the orientation of the level set function evolution. In this study, the authors improved the conventional distance regularized level set evolution method, introduced new edge indicator functions and proposed an oriented distance regularized level set evolution method for image segmentation. The experiment results show the proposed method has a better segmentation result in images with multiple boundaries. Moreover, alternately selecting the edge indicator functions we proposed during the level set evolution can lead the zero level set contour to converge to the desired boundaries in complicated images.

中文翻译：

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用于图像分割的定向距离正则化水平集演化

传统的距离正则化水平集演化方法在图像分割中已经非常流行，但是当图像中存在多个不需要的边界时，通常无法收敛到所需的边界。通过观察，目标边界和不需要的边界之间的梯度方向通常在一幅图像中是不同的。边界的梯度方向信息可以指导水平集函数演化的方向。在这项研究中，作者改进了传统的距离正则化水平集演化方法，引入了新的边缘指示函数，并提出了一种用于图像分割的有向距离正则化水平集演化方法。实验结果表明，该方法在多边界图像中具有较好的分割效果。而且，