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首页> 外文会议>International Conference on Frontiers of Intelligent Computing : Theory and Applications >Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing
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Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing

机译：基于边缘探测器的自动初始边界生成方法，用于春VESE分段模型和生物医学图像处理中的应用

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摘要

Image segmentation is an important problem in image processing that has a wide range of applications in medicine, biomedicine and other fields of science and engineering. During the non-learning-based approaches, the techniques based on the partial differential equations and calculus of variation have attracted a lot of attention and acquired many achievements. Among the variational models, the Chan-Vese variational segmentation is a well-known model to solve the image segmentation problem. The level set methods are highly accurate methods to solve this model, and they do not depend on the edges. However, the performance of these methods depends on the level set function and its initial boundary too much. In this paper, we propose automatic initial boundary generation methods based on the edge detectors: Sobel, Prewitt, Roberts and Canny. In the experiments, we prove that among the four proposed initial boundary generation methods, the method based on the Canny edge detector brings the highest performance for the segmentation method. By combining the proposed initial boundary generation method based on the Canny edge detector, we implement the Chan-Vese model to segment biomedical images. Experimental results indicate we obtain improved segmentation results and compare different edge detectors in terms of performance.
机译：图像分割是图像处理中的重要问题，其在医学，生物医学和其他科学和工程领域具有广泛的应用。在基于非学习的方法期间，基于部分微分方程和变异微积分的技术引起了很多关注并获得了许多成就。在变分模型中，CHAN-VESE变分分割是解决图像分割问题的众所周知的模型。级别设置方法是解决此模型的高准确方法，它们不依赖于边缘。但是，这些方法的性能取决于水平集函数及其初始边界。在本文中，我们提出了基于边缘探测器的自动初始边界生成方法：Sobel，Prowitt，Roberts和Canny。在实验中，我们证明，在四个提出的初始边界生成方法中，基于Canny Edge检测器的方法为分割方法带来了最高性能。通过组合基于Canny Edge检测器的所提出的初始边界生成方法，我们将Chan-Veses模型实施到分段生物医学图像。实验结果表明，我们获得了改进的分割结果，并在性能方面比较了不同的边缘探测器。

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