Multilevel thresholding selection based on variational mode decomposition for image segmentation

Multilevel thresholding techniques based on gray histogram are usually computationally expensive for the image segmentation. In this paper, we propose a novel thresholding extraction method based on variational mode decomposition (VMD). The improved VMD is used to decompose the histogram non-recursively into several sub-modes for minimizing Otsu's objective function. Then, we can extract the thresholds easily by the minimum point search (MPS) method or the cross point search (CPS) method. The experimental results demonstrate that the proposed MPS scheme exhibits more excellent capability than CPS. Further, compared with other approaches namely particle swarm optimization algorithm (PSO), fuzzy c-means (FCM) algorithm and bacterial foraging (BF) algorithm, the proposed algorithm can get similar performance, but its computing speed is faster than others. Therefore, it could have some advantages in image preprocessing, such as fast target recognition and classification.