Automatically segmenting rock CT images is difficult because of the low resolution and fuzzy edges of rock CT images. To achieve desired results

human intervention is necessary in the segmentation process. However

the CT image data for 3D model reconstruction is considerably large. In this case

the workload becomes too heavy to achieve a successful intervention for each frame. In this work

we propose an efficient and practical method that can ensure desired segmentation results with little human intervention. An original threshold is first set. This original threshold comes from one of the CT images used in the study. Then

the threshold for segmenting adjacent frames is calculated automatically using the correlation of the adjacent frames. In this way

all the segmentation objects from the rock CT images are successfully obtained. If necessary

all the CT images are repaired with an automatic region growing method according to the frame with perfect segmentation to improve segmentation. Manual repair is found to be useful when automatic repair is unsuccessful. After the manual repair of some of the objects in one frame

the correlative targets of the other frames are segmented automatically with the proposed method. Improved results are obtained. By testing a series of rock CT images with various scales and resolutions

we find that the method proposed in this work can achieve the desired segmentation at speeds equal to that of the segmentation method based on a fixed threshold and double that of the OSTU method. The proposed segmentation method for rock CT images maximizes the correlation of adjacent frames and can quickly and efficiently extract objects from continuous images in sequence.