Table 1 presents the evaluation results using the four region overlap metrics; it can be seen that the proposed MLBF model has the optimal performance indicators.
The speed of evolution convergence of the MLBF model can be reflected by the number of iterations required to obtain the final target contour and the CPU time required to complete the entire segmentation process.
Figure 8 shows the results of this set of comparison experiments, where the first row is the input images and the initial contours of the LSE process, and the second to the fifth rows are the segmentation results by using the LBF, LGDF, LIF, and our MLBF models, respectively; here, we use the usual form of the level set method to express the final segmentation results.
In this paper, we propose a level set segmentation model called MLBF. By introducing multiscale ideas into the classical LBF model, the MLBF model in this paper achieves excellent segmentation performance in the segmentation experiments of inhomogeneous images.