Registration of ultrasound images is often complicated due to inherent noise. Robust similarity metrics and optimization procedures are required to facilitate medical applicability. In this paper a novel hybrid procedure, incorporating global statistics and local textural features, is proposed for the registration of envelope detected radio frequency ultrasound data. On the global scale this is achieved by Hellinger distance between distribution in images, and on the local scale by a statistics-based extension of Fuzzy Local Binary Patterns (FLBP). The proposed procedure is shown to outperform standard measures such as SSD and NCC, as well as Hellinger distance and histogram matching of standard FLBPs, in rigid registration experiments of envelope detected radio frequency data samples of the human neck.