Many natural or texture images contain structures that appear several times in the image. One of the denoising filters that successfully take advantage of such repetitive regions is the nonlocal means filter. It is simple and yields very good denoising results. Unfortunately, the block matching within the standard nonlocal means filter is not able to handle rotation or mirroring. Rotated or mirrored instances are not detected as variations of the corresponding original structures. In this paper, we analyse two natural approaches for a rotationally invariant similarity measure that will be used as an alternative to, respectively a modification of the well-known block matching algorithm in nonlocal means denoising. The first approach is based on similarity distances computed with the help of moment invariants whereas the second one estimates the rotation angle, rotates the block via interpolation and then uses a standard block matching. In contrast to the standard method, the presented algorithms can find similar regions or patches in an image even if they appear in several rotated or mirrored instances. With this modification, the nonlocal means filter is able to find more suitable regions for its weighted average.