In this paper, a level set segmentation algorithm based on Bayesian classification for medical image segmentation was proposed.
本文提出了一种结合贝叶斯分类的水平集方法用于医学图像分割。
Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation.
水平集方法应用于图象分割的曲线或曲面进化问题，是一种稳定有效的数值计算方法。
The image segmentation method based on level set active contour model is widely concerned by domestic and foreign scholars for its superior performance, flexible structure, and diverse forms.
基于水平集活动轮廓模型的图像分割方法凭借其优越的性能、灵活的结构、多样的形式被国内外学者广泛关注。
This paper have a study on image segmentation which is based on level set method.
本文研究了基于水平集方法的图像分割方法。
Variational Level Set Methods for Multiphase Image Segmentation and for Image Segmentation Based on Prior Shape;
提出了一种基于水平集方法的结构刚度拓扑优化设计方法。
Variational Level Set Methods for Multiphase Image Segmentation and for Image Segmentation Based on Prior Shape;
提出了一种基于水平集方法的结构刚度拓扑优化设计方法。
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