With the increasingly important role of image segmentation in the field of computed tomography (CT) image segmentation, the requirements for image segmentation technology in related industries are constantly improving. When the hardware resources can fully meet the needs of the fast and high-precision image segmentation program system, the main means of how to improve the image segmentation effect is to improve the related algorithms. Therefore, this study has proposed a combination of genetic algorithm (GA) and Great Law (OTSU) algorithm to form an image segmentation algorithm-immune genetic algorithm (IGA) algorithm. The algorithm has improved the segmentation accuracy and efficiency of the original algorithm, which is beneficial to the more accurate results of CT image segmentation. The experimental results in this study have shown that the operating efficiency of the OTSU segmentation algorithm is up to 75%. The operating efficiency of the GA algorithm is up to 78%. The operating efficiency of the IGA algorithm is up to 92%. In terms of operating efficiency, the OTSU segmentation algorithm has more advantages. In terms of segmentation accuracy, the highest accuracy rate of OTSU segmentation algorithm is 45%. The accuracy of the GA algorithm is 80%. The highest accuracy of the IGA algorithm is 97%. The IGA algorithm is more powerful in terms of operating efficiency and accuracy. Therefore, the application of the IGA algorithm to CT image segmentation is beneficial to doctors to better judge the lesions and improve the diagnosis rate.