Abstract: A novel method is introduced for medical image segmentation based on the gradient vector flow.Experiments demonstrate that this method can increase the capture range of the deformable contour.After several iterations，the method can move the deformable contour into the boundary，which is close to the real boundary.In addition，the method is robust and can be applied to noisy images，which is applicable for medical images.This method is applied to segment corpus callosum from the brain in MRI images and it is effective and speedy to achieve a good result of great diagnostic value.

Key words: deformable models, gradient vector flow, image segmentation, corpus callosum

摘要： 介绍一种基于梯度向量流场的医学图像分割方法。无论初始轮廓线位于真实边界以内或以外，变形轮廓都具有较宽的作用范围以及良好的收敛性，经过迭代算法后可以得到与真实图像边界十分接近的最终变形轮廓。此外，该方法对噪声图像也表现出良好的鲁棒性，特别适用于医学图像分割场合。将该方法应用于MRI图像上胼胝体的分割提取，实验结果表明，与传统手工方法相比，应用梯度向量流场方法提取出的胼胝体轮廓清晰，效果良好，而且耗时大为降低，这在临床应用中具有积极意义。

关键词: 变形模型, 梯度向量流场, 图像分割, 胼胝体