Statistical shape and intensity modelling have been subject to an increasing interest within the past decade. However, construction of such models requires large number of segmented examples. Accurate and automatic segmentation techniques that do not require any explicit prior model are therefore of high interest. We propose a fully-automatic method for segmenting the femur in 3D Computed Tomography (CT) volumes, based on graph-cuts and a bone boundary enhancement filter analysing the second-order local structure. The presented technique is evaluated in large-scale experiments, conducted on 197 femur samples, and compared to other three automatic bone segmentation methods. Our approach achieved accurate femur segmentation in 81% of cases without any shape prior or user interaction.