Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguistic scope. Hedge scope is a sequence of tokens including the hedge cue in a sentence. Previous hedge scope detection methods usually take all tokens in a sentence as candidate boundaries, which inevitably generate a large number of negatives for classifiers. The imbalanced instances seriously mislead classifiers and result in lower performance. This paper proposes a dependency-based candidate boundary selection method (DCBS), which selects the most likely tokens as candidate boundaries and removes the exceptional tokens which have less potential to improve the performance based on dependency tree. In addition, we employ the composite kernel to integrate lexical and syntactic information and demonstrate the effectiveness of structured syntactic features for hedge scope detection. Experiments on the CoNLL-2010 Shared Task corpus show that our method achieves 71.92% F1-score on the golden standard cues, which is 4.11% higher than the system without using DCBS. Although the candidate boundary selection method is only evaluated on hedge scope detection here, it can be popularized to other kinds of scope learning tasks.