由 Kass等人提出的主动轮廓模型，本质上是一条能量最小化的轮廓曲线.它作为一种全新的采用自上而下机制的图象目标提取方法，由于它有效地利用了高级信息，从而提高了目标提取的速度和准确性，已经在数字图象处理和计算机视觉领域得到了广泛的应用.原始的主动轮廓模型算法可以分为构造能量函数、推导欧拉方程、离散化和迭代求解 4步.但该算法存在许多问题，为此在分析原始主动轮廓模型算法和一些改进算法的基础上，提出了一种基于遗传算法的主动轮廓模型算法，并给出实验结果.实验结果证明，基于遗传算法的主动轮廓模型不仅成功地解决了原方法收敛易陷入局部最小值的问题，也提高了目标提取的成功率.

Active Contour Model introduced by Kass et al is a energy-minimizing curve in essential. It is a new method of image object extraction based on top-down mechanism, which makes use of high level information to improve the speed and veracity of object extraction. It has been used more and more widely in applications of image analysis and computer vision. The original algorithm of active contour model involves four steps: setting up a variational integral on the continuous, deriving a pair of Euler equations, discretizing them, and solving the discrete equations. This algorithm suffers a number of problems. In this paper, we will firstly discuss the original algorithm and some improved algorithms of active contour model, then propose a algorithm based on the genetic algorithm and present the experiment result. The result proves that genetic algorithm settles the problem of original model that run into the local least value end enhance the success ratio of the object extraction.