Design of fusion rule is an important step in fusion process. Traditional single fusion rules are inflexible when they are being used to fuse feature-rich images. To address this problem, an adaptive multistrategy image fusion method is proposed. Its flexibility lies in the combination of a choose-max strategy and a weighted average strategy. Moreover, the region-based characteristics and the shift-invariant shearlet transform (SIST)-based activity measures are proposed to guide the selection of strategies. The key points of our method are: (1) Window-based features are extracted from the source images. (2) Use of the fuzzy c-means clustering algorithm to construct a region map in the feature difference space. (3) The dissimilarity between corresponding regions is employed to quantify the characteristic of regions and the local average variance of the SIST coefficients are considered as activity measures to evaluate the salience of the related coefficient. (4) The adaptive multistrategy selection scheme is achieved by a sigmoid function. Experimental results show that the proposed method is superior to the conventional image fusion methods both in subjective and objective evaluations.