Due to the different clarity of the cells in different layers of the cellular images acquired by professional CT equipment, it is necessary to consume a large amount of manpower, material, and time costs to organize and archive the acquired data in case of too many layers, which seriously affects the work efficiency of the relevant workers. Currently, there is no mature solution for segmenting and sampling cell images from different CT layers and combining them into clearer images without changing the original data. In order to address the above problems, we propose a feasible and efficient solution for the whole process of combining CT images. Firstly, we propose and complete three feasible sampling methods for CT images, and use the four-channel representation method for relational retrieval and classification. At the same time, we use objective quantitative indexes to objectively select the cells with the highest clarity and combine them at a very low time cost. In our experiments, 20 layers of CT images with a resolution of 9391 × 9391 are sampled simultaneously, and the three proposed schemes can improve the average gradient by up to 148.0%, the variance by up to 110.0%, and the NIQE by up to 50.8% compared with the original images, and the total time consumed can be controlled within 68.9 s.