Based on the level set method without re-initialization, a sequential level set method is proposed to realize full image segmentation. The proposed method automatically and alternatively creates nested sub-regions or the corresponding initial level set functions in the image to be segmented, and then makes the level set function evolved to be convergence in the corresponding sub-region. This step is sequentially repeated until the sub-region vanishes. Compared with the original method and a representative region-based level set method, the proposed method has many advantages as follows: 1) It is automatically executed and does not need the interactive initialization anymore; 2) It segments image more than once and detects more boundaries than the original method; 3) It can get better performance on non-homogenous images than the representative region-based level set method; 4) It is an open image segmentation framework in which the single level set method is used can be replaced by other single level set methods after some modification. Experimental results indicate that the proposed method could fully segment the synthetic and medical images without interactive step and additionally works more robust on non-homogenous images.