Skeleton pruning is an important issue in skeleton extraction and application. A common pruning approach utilizes the thresholding of skeletal components by saliency indices based on region reconstruction. However, this approach suffers difficulties in algorithmic parameter setting, pruning outcome control, and the execution time. To deal with these difficulties, a pruning method is proposed that iteratively removes the skeletal components. The punctuating skeleton length saliency index is used, and in each iteration, the least salient skeleton branch is pruned out, until the number of the remaining branches reaches a user defined level. In order to accelerate the algorithm, the RunForest data structure is adopted for region reconstruction operations, and the reconstruction triggering strategy (RTS) is proposed to reduce the number of reconstructions needed. Experimental results on a real-world image base show that the recall of the skeletal branches of the proposed method is higher than the existing algorithm by 13 percentage points, and the precision, by about 3 points. The execution time of the algorithm with RTS is about 56% that of without. The results show that the proposed method is effective.