Multiple entropy thresholding （MET） increases exponentially with an increase in the number of thresholds K. Related optimization strategies exhibit low accuracy and stability with the segmented aggregate images lacking considerable feature information such as surface roughness and edges. To overcome these problems， an automatic image segmentation model based on a chaotic sparrow search algorithm （SSA） was developed to optimize MET. SSA is a newer intelligent optimization algorithm. To enhance the global optimization capability and robustness of SSA， a logistic map is added to the uniform sparrow distribution at the time of population position initialization， an expansion parameter is applied to expand the global search， and temporal local stagnation is avoided by range-control elite mutation jumps. This algorithm is called logistic SSA （LSSA） and can improve the solution quality without reducing convergence speed. LSSA is used for the automatic selection of MET parameters， with the Renyi entropy， symmetric-cross entropy， and Kapur entropy as objective functions to quickly determine the correct thresholds. In this study， image segmentation and algorithm comparison experiments are conducted on aggregate images with different characteristics. The effectiveness of LSSA-MET was demonstrated by comparing six types of combined algorithms with the fuzzy C-means （FCM） algorithm. The proposed algorithm maintains a relatively high speed with an increase in K， taking 1.532 s to split an image on average even when K=6. Among the variousm entropies， LSSA-Renyi entropy performed the best， achieving 29.92%， 10.67%， and 5.16% accuracy improvements in peak signal-to-noise ratio （PSNR）， structural similarity （SSIM）， and feature similarity （FSIM）， respectively， thereby effectively retaining the aggregate surface texture and edge characteristics while achieving the optimum balance between precision and speed.