We presented a novel and fast convergent skeleton snake model based on gradient vector flow (GVF).
提出了一种基于梯度矢量流(GVF)的快速收敛骨架snake算法。
After analyzing snake and geometric active contour model, a minimum variation snake model was proposed and successfully applied to weak edge medical image segmentation.
我们在分析参数活动轮廓和几何活动轮廓模型的基础上，提出最小方差参数活动轮廓模型，并成功应用于医学图像自动分割。
In this paper we propose a new ultrasound image segmentation scheme for segmentation of low SNR ultrasound images, which consisted of anisotropic diffusion function and snake model.
为了对低信噪比的超声图像进行有效分割，提出了一种新的超声图像分割方案，该方案由各向异性扩散方程和蛇模型组成。
SNAKE model is mostly applied in medical image manipulation, motion track, shape recognition, and so on.
SNAKE模型主要应用在医学图像，运动跟踪，形状识别等领域。
Designing external force in the form of GVF can expand the scope of mapping to make Snake model towards to expected image features.
将外力设计成GVF场的形式，可扩大图像的映射范围，使模型更好地向所期望的目标特征拟合。
On the basis of analyzing the Snake model and improved model, the adaptive active contour model based on distance equalization is proposed.
本文在分析参数活动轮廓模型及其改进模型的基础上，提出了基于距离均衡化的自适应动态轮廓模型。
After that we studied several Post-processing models based on PDE of Anisotropic Diffusion, including Directional Diffusion and Self-Snake model.
进而深入研究了经典的基于偏微分方程的后处理方法，包括方向扩散，自蛇模型等非线性扩散方程。
With the introduction of high level information, Snake model has more satisfactory effects than what gained by the traditional method when dealing with disconnected edges in the image.
主动轮廓线模型引入高层知识，在处理局部间断的边缘时，具有优于传统轮廓提取方法的提取效果。
With the introduction of high level information, Snake model has more satisfactory effects than what gained by the traditional method when dealing with disconnected edges in the image.
主动轮廓线模型引入高层知识，在处理局部间断的边缘时，具有优于传统轮廓提取方法的提取效果。
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