Particle Segmentation Algorithm for Core Stereo Microscopic Images Based on Optimizational NCUT

In order to overcome the problems of traditional NCUT such as low efficiency,poor resolution and poor adaptability,this paper proposed an algorithm for particle segmentation based on the characteristics of core stereo microscopic images.In this paper,the detected high-light pixels were replaced by the minimum value of the luminance in the neighborhood of the pixel to eliminate the image highlight;then the second watershed algorithm was used to segment image,and the resulting regions were mapped to the NCUT input nodes,so the number of nodes were reduced by the block point,and the segmentation efficiency was improved;and also the NCUT weight matrix was redesigned by introducing edge gradient feature and using adaptive Gaussian kernel scale factors to improve segmentation accuracy and adaptability;finally,the constraint condition was used to determine the K-means initial clustering centers to improve clustering stability.All above achieved the optimization of particle segmentation algorithm.The results showed that the optimized NCUT reduced the dependence on the initial parameters,had better segmentation effect,and shortened the computation time obviously.