secondly, the proposed method should use ADPLS method mainly to be fast and robust when the zero level set evolves in the intensity homogeneity area and use LBF model mainly to segment image accurately when the zero level set evolves near the objective boundary.
With bigger weight, the ADPLS method will lead the evolution of level set.
Accordingly, the weight of the LBF model will increase and be even larger than that of the ADPLS method.
The parameters used in ADPLS method are [[DELTA].sup.GIF] = 1.0, [[epsilon].sub.1] = 1.5, [sigma] = 2.0, and the parameter of punishment [beta] = 0.2/[[DELTA]t.sup.GIF], [alpha] = 10; the value of c should be small when dealing with simple
In our experiments, the ADPLS method fails to segment the object correctly after 1000 iterations and 24.453 seconds.
Column 1 shows the initial contours, and Columns 2, 3, and 4 show the results of the LBF model, the ADPLS method, and the proposed method, respectively; they all have the same initial contours in column 1.
The fusion method proposed in the paper uses a variable weighting coefficient to combine both ADPLS method and LBF model.
On the other hand, compared with the ADPLS, the proposed method can fully avoid boundary leakage and lack of segmentation with high speed when processing images with intensity inhomogeneity.