Microaneurysms (MAs) are the earliest lesions diabetic retinopathy (DR), and the accurate MA detection can assist in early diagnosis of diabetes. Many effective detection techniques have been proposed recently, the interference of blood vessels and blurry boundaries of MAs reduce the performance of these methods. To address these problems, this paper proposes an accurate MA detection method. In the candidate detection stage, a shape suppression filter is proposed to remove strip-shaped blood vessels and retain the dark blobs. In order to enhance blurry boundaries of MAs, we apply the additive bias correction level set to locate circular regions. In the next stage of feature extraction, a new set of features based on gray level co-occurrence matrix (GLCM) is extracted for each candidate to discriminate the MAs from non-MAs candidates using the random undersampling boosting (RUSBoost) classifier. We test our method on four public datasets, resulting in the optimal performance to the existing methods. It achieves average sensitivity values of 0.672, 0.721, 0.602 and 0.735, respectively.