Medical image segmentation based on improved fuzzy clustering in robot virtual surgical system

In view of the problems relating to the precision and convergence rate of traditional ant colony algorithm and fuzzy clustering algorithm on the medical image segmentation, a modified selfadaptive threshold ant colony optimization and fuzzy clustering (SAAF) algorithm were proposed here to realize the segmentation of the complex background medical image. As to the complex medical image, Otsu algorithm was firstly performed to obtain the optimal threshold, the local optimal solution of ant colony algorithm was avoided through the intervention of optimal threshold, then the clustering center and the number of cluster classes obtained through the selfadaptive threshold ant colony algorithm were imported into the fuzzy clustering algorithm until the image segmentation was finalized. The doctor can get the diseased organ by SAAF image segmentation algorithm according to the CT scanned images, and can use the segmented organ image to build the 3D virtual organ tissue model. Then, the doctor can achieve virtual surgical before using the real robot surgical to operate on patients. It will greatly improve the success rate of surgery and efficiency.