Multiphase Image Segmentation Model Based on Clustering Algorithm

In the traditional Multiphase Image segmentation algorithm, there are some problems, such as long operation time, high calculation cost and high error rate. In order to improve the above problem, originally proposed a multiphase image segmentation enhancement algorithm and model based on the combination of clustering algorithm. In this paper, for the problem of large amount of initial calculation for the segmentation model of multiple targets, a clustering algorithm is proposed to segment the image, and finally the model is established, the function data set is initialized, and the method of multiphase image segmentation is easier to segment idealized goal. This method can reduce the sensitivity of the clustering algorithm in the initialization, making the multiphase image segmentation model under the clustering algorithm easier to segment the ideal image. At the same time, the image segmentation model can quickly get the minimum value, reduce the amount of calculation, fully and effectively improve the efficiency. The M-level set implicit surface of multi-level set function is used to divide the image into m regions. The maximum value of level setting function is calculated to realize fast segmentation and reconstruction of constant value of multiphase segmentation. Experimental results show that the algorithm can greatly improve the contrast and clarity of images, and bring the best visual experience to people. Compared with the traditional clustering model, it has less iteration steps and faster segmentation speed.