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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.