Abstract: Accurate segmentation of liver CT images provides an objective basis for liver disease research and early diagnosis. It is also a crucial traceable project in medical metrology. This paper introduces an improved automatic segmentation method for liver CT images using a region growing approach, detailing the processing steps and implementation scheme. In the enhanced segmentation method, noise is first removed and image information is enhanced through threshold segmentation and nonlinear mapping. This method was applied to segment nine randomly selected abdominal CT image sequences, and the results were compared with those obtained using the threshold method. The Quasi-Monte Carlo (QMC) method is employed for seed point selection to optimize the region growing conditions. Finally, morphological methods combined with the Canny operator and flood fill algorithm are used for post-processing to smooth the liver contour and segmentation results. The results show that, compared to the threshold method, the region growing method achieves a segmentation accuracy of up to 91.89%, allowing for more precise liver segmentation. Furthermore, the volume overlap error (VOE) and relative volume difference (RVD) statistics of the segmentation results verify the effectiveness and stability of this method, indicating that it can be effectively applied to liver CT image segmentation and provide reliable support for the diagnosis of liver diseases in clinical medicine.