基于边带限制的梯度矢量流主动轮廓线模型的超声图像分割

主动轮廓线模型是广泛应用于数字图像分析和计算机视觉等领域的一种目标轮廓跟踪算法，非常适合于医学图像（如CT和MRI）的处理。但将这一模型应用于超声图像的分割和目标轮廓的跟踪时，由于超声图像不可避免地存在着斑点噪声、弱边界和与组织有关的纹理，往往使传统主动轮廓模型难以获得满意的轮廓跟踪效果。为此，在梯度矢量流主动轮廓线模型的基础上，引入边带限制概念，并将该模型应用于超声图像的分割。实验表明，该方法较好地限制了非目标边缘和噪声干扰的影响，而且对超声及其序列图像具有较好的分割效果。

Band Limitation-Based Gradient Vector Flow Active Contour Model for Ultrasound Image Segmentation

Active contours, or snakes, are used extensively in digital image analysis and computer vision applications, particularly to locate object boundaries, which is very useful in medical image processing, such as MRI and CT images. However, due to speckle noises, weak edges and tissue related textures in ultrasound images, the conventional active contour models usually can not obtain satisfying contour tracking results. Based on gradient vector flow(GVF) active contour model, this paper introduced a conception of band limitation and applied this active contour model into ultrasound image segmentation. The experimental results show that this algorithm limits the influence of false edge and noise disturbance and obtains desired segmentation results for ultrasound and ultrasound serial images.