The original purpose of Snakesis is for image segmentation. The method suffers from a strong sensitivity to its initial position and can not deal with topological changes. Its sensitivity to initialization can be overcame by the genetic algorithms (GAs). The GAs is a global optimal searching algorithm and has better numerical stability. But its disadvantages are the computational complexity and the rapid increasing of computation by the augmentation of the search space. They both affect the convergence rate of the GAs. This paper presents an image segmentation method of Dual T Snakes model based on the GAs. By making use of the Dual T Snakes model

it inherits the capability of changing the topology of the T Snake

reduces the valid search space for the GAs to remedy its limitations. The solution of the Dual T Snake consists of two curves enclosing each object boundary

and it is composed the valid search space of the GAs. The optimal object boundary can be obtained through the operation of selection

crossover

and mutation. The new model can accelerate the convergence rate while inheriting the capability of changing the topology of the T Snake

avoid local minima from Snakes model

and maintain the global optimal ability of the GAs

then obtain more precise segmentation. Better results are achieved in application of this method on segmentation of cardiac magnetic resonance images.