In recent years

the fields of active-contour based image segmentation have seen the emergence of two competing approaches.The first is based on parameter models and the other is based on geography models.Snake model can segment objects quickly

but can not deal with topological changes and sensitive to initialization.Level Set model can deal with topological changes but it's time efficiency is low.After comparing these two approaches

this paper presents a new active contour model:S-L model

which combines the virtues of Snake model and Level Set model.The new model uses the energy equation of Snake model to evolve the curve and uses a symbol table

which is based on the soul of Level Set model

to change the topology of the curve.To reduce the effect of the noise

the new model constructs a new outer force on the basis of the region information.With the new outer force

the initial curve can be made in a large space. With the region information

the new model can find the edges powerfully

even if in case of complex topology

avoid local minima from Snake model.The experiments to segment cardiac magnetic resonance images show that comparing with Level Set model the new model can get the similar results which has mach higher speed.