Fan Yong, Jiang Tianzi, Evans David J
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
IEEE Trans Med Imaging. 2002 Aug;21(8):904-9. doi: 10.1109/TMI.2002.803126.
Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performance, a parallel genetic algorithm-based active model method is proposed and applied to segment the lateral ventricles from magnetic resonance brain images. First, an objective function is defined. Then one instance surface was extracted using the finite-difference method-based active model and used to initialize the first generation of a parallel genetic algorithm. Finally, the parallel genetic algorithm is employed to refine the result. We demonstrate that the method successfully overcomes numerical instability and is capable of generating an accurate and robust anatomic descriptor for complex objects in the human brain, such as the lateral ventricles.
基于活动模型的分割在医学图像处理中经常被使用，并取得了相当大的成功。尽管基于活动模型的方法最初被视为一个优化问题，但大多数研究人员将其作为偏微分方程解来实现。基于活动模型的方法的优点和缺点很明显：速度和稳定性。为了提高其性能，提出了一种基于并行遗传算法的活动模型方法，并将其应用于从磁共振脑图像中分割侧脑室。首先，定义一个目标函数。然后使用基于有限差分法的活动模型提取一个实例表面，并用于初始化并行遗传算法的第一代。最后，使用并行遗传算法对结果进行优化。我们证明该方法成功克服了数值不稳定性，并且能够为人类大脑中的复杂对象（如侧脑室）生成准确且稳健的解剖描述符。
