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介绍了一种结合live wire算法和活动轮廓模型的医学图像序列的分割方法.通过把live wire算法和图像分割中一般的区域增长方法结合,对传统live wire算法进行了改进,并用改进后的算法对医学图像序列中的单张或多张切片进行交互式地准确分割.然后计算机利用活动轮廓模型自动分割相邻的未分割切片.还通过在活动轮廓模型的边缘点中引入记录已分割物体边缘附近局部区域特征的灰度模型,把已分割切片中的物体与背景的局部区域特征带入相邻的未分割切片中,并用由灰度模型定义的区域相似性代替活动轮廓模型中的外能来引导边缘轮廓收敛到物体的实际边缘.最后介绍了一种基于live wire算法思想的简单的分割结果交互式修复方法.实验结果表明该算法仅需少量用户交互就能快速准确地从医学图像序列中分割出感兴趣的物体,在医学图像分析中具有实用价值.“,”In this paper, an algorithm based on the combination of the live wire algorithm and the active contour model is proposed for the semiautomatic segmentation of medical image series. The traditional live wire algorithm is modifiedby integrating with the fuzzy region growing method. Then the improved live wire algorithm is applied to obtain accurate segmentation of one or more slices in a medical image series. Next, the computer will segment the nearby slice automatically using the active contour model. To record the local region characters of the desired object in the segmented slice, a gray-scale model is introduced to the boundary points of the active contour model. Based on the similarity measure of regions in the gray-scale model, a new energy function is defined to replace the external energy of the traditional active contour model. Finally, a simple method based on the idea of the live wire algorithm is introduced for the reparation of the automatic segmentation result to guarantee the reliability of the result. Experiment shows that this algorithm can obtain the boundary of the desiredobject from a series of medical images quickly and reliably with only little user intervention. It has practical value in the medical image analysis.