Maximally stable extremal regions (MSER) is a state-of-the-art method in local feature detection.However,this method is sensitive to blurring because in region boundary the intensity value will vary more slowly, which undermines the stability criterion the MSER relies on.In this paper, we propose a method to improve the MSER method,making it more robust to image blur.To find back in the blurred image the regions missed by MSER, we utilize the fact that the entropy of the probability distribution function of intensity values increase rapidly when the local region expands across the boundary, while the entropy in the central part keeps small.We use the entropy averaged by region area as a measure to re-estimate regions missed by MSER.Experiments show that when dealing with blunred images, our method has better performance than the original MSER, with little extra computational effort.