基于细胞神经网络的肝脏核磁共振图像自动分割方案

Automatic Liver Segmentation Scheme for MRI Images Based on Cellular Neural Networks

Currently, the processing speed of existing automatic liver segmentation for Magnetic Resonance Imaging (MRI) images is relatively slow. An automatic liver segmentation scheme for MRI images based on Cellular Neural Networks (CNN) is presented in this paper. It ensures the validity of this scheme and at the same time completes the image segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN image processing, firstly, three-dimensional liver MRI images should be transformed into binary images; secondly, an appropriate template parameter of the Global Connectivity Detection CNN (GCD CNN) shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN (HF CNN) are used to repair the entire extracting liver and improve the accuracy of liver segmentation; finally, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic segmentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.