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The quantification of cancellous bone network from computed tomography (CT) images requires a segmentation step which is crucial and difficult because of the partial volume effect in CT images. In this paper, we present and evaluate a new approach for segmenting cancellous bone network from high-resolution CT (HRCT) slices. The idea is first to detect a skeleton from the crest lines of the structure and then to thicken it to extract the whole bone structure by satisfying local neighborhood constraints. The segmentation requires the adjustment of relative and not absolute parameters like most methods. We quantified the influence of these parameters on architectural measurements. Results were first validated by using a physical phantom and then examined on a series of 12 HRCT images of human lumbar vertebra of different ages. We demonstrated that the choice of segmentation parameters yielded important variability on architectural measurements (up to 20%), but less variability than a more commonly used approach. This stresses the importance of settle on the segmentation parameters for once, which is possible with the proposed method.