Liver Image Segmentation Algorithm Based on RBF Confidence Interval

For the characteristics of liver images and the shortcomings of the traditional region growing algorithm, a liver segmentation method based on RBF-CI (RBF-Confidence Interval) is proposed. On the basis of the application of window adjusting technology and the anisotropic diffusion method, the RBF neural network learning algorithm is introduced to calculate the coefficient of the confidence interval in order to reduce users' interaction amount and realize adaptive liver image segmentation. Simulation results show that, the proposed RBF-CI region growth segmentation algorithm can achieve effective segmentation results of liver images. For the ten sets of the liver images, while our proposed segmentation algorithm result achieves an average accuracy rate of 90.86%, which indicates the segmentation is accurate.