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Aiming at the problem of the computational complexity and low segmentation precision of the multi-threshold image segmentation method, this paper proposed an ISCA based multi-threshold image segmentation method. Firstly, this method used a chaotic initialization technique to improve the quality of initial population. Secondly, it introduced an adaptive strategy to adjust the parameters according to the fitness values. Finally, it utilized an opposition-based learning strategy, then selected the better particles. The results of the Berkeley image and the plant canopy image segmentation experiments show that this method has a satisfied performance in terms of running time and segmentation accuracy. And it has a strong robustness. [ABSTRACT FROM AUTHOR]