The study and application of the improved region growing algorithm for liver segmentation

In order to improve the accuracy of the medical image segmentation and reduce the effect of selecting seed points using region growing algorithm, an improved region growing method is proposed in this paper. First, the source images are pre-processed using non-linear mapping method and the region of interest in the liver is selected by man–machine interaction; Quasi-Monte Carlo method is used for generating low-dispersion sequences points in the region of interest and the optical seed points are selected by computing these points; In addition, the region growing criteria is also improved. The improved region growing algorithm is used for segmenting three discontinuous abdomen CT images. Compared with the traditional region growing method, the improved method can get better liver segmentation effects. The proposed method can be effectively applied to liver segmentation and it can improve the accuracy of liver segmentation.