Nonlocal Means is an effective denoising method, which takes advantage of the fact that natural image has selfsimilarity. However, the original nonlocal means may not find enough similar candidates for some non-repetitive image blocks. In order to mitigate these drawbacks, we propose an improved nonlocal means method using adaptive preclassification in this paper. The proposed method employs the threshold-based clustering algorithm to classify noisy image blocks adaptively. Then, a rotational block matching method is adopted to find the appropriate distance measurement between two blocks in an image. Experimental results on a set of well-known standard images show that the proposed method is effective, especially when the image contains large amount of noise.