An adaptive multi-level-sets active contour model based on block search

In order to better handle images with intensity inhomogeneity and noise, an adaptive multi-level set active contour model based on block search is proposed in this paper. This model first defines a multiple edge extension criterion for the input image to block the image and avoid the loss of image edge information; Then, the proposed adaptive block search method is used to find the level set that is considered redundant, and the remaining parts are fused to obtain a rough binary mask; Finally, the target is extracted by using the newly defined energy functional and the edge contours of the extracted binary mask. The experimental results show that the average jaccard similarity coefficients of the proposed model for segmenting images with intensity inhomogeneity and real images are 97.81% and 98.41%, respectively, and the accuracy of the segmentation results is higher than that of other models participating in the comparison. Similarly, the results of the ablation experiment once again validated the robustness of the proposed model.