In full or arbitrary field-of-view (FOV) 3D CT imaging, obtaining an accurate per-voxel segmentation for complete large and small bones remains an unsolved and challenging problem. The difficulty lies in the notable variation in appearance and position observed among cortical bones, marrow and pathologies. To approach this problem, several studies have employed active shape models and atlas models. In this paper, we argue that a bottom-up approach, defined by classifying and grouping supervoxels, is another viable technique. Moreover, it can be integrated into a conditional random field (CRF) representation. Our approach consists of the following steps: first, an input CT volume is decomposed into supervoxels, in order to ensure very high bone boundary recall. Supervoxels are generated via a robust process of conservative region partitioning and recursive region merging. In order to maximize sparsity and classification efficiency, we use a Bayesian sparse linear classifier to compute and optimize middle-level image features. Next, we disambiguate the CRF unary potentials via contextualized optimization by pooling over selective supervoxel pairs. Finally, we adopt a pairwise support vector machine (SVM) model to learn the CRF pairwise potential in a fully supervised manner. We evaluate our method quantitatively on 137 low-resolution, low-contrast CT volumes with severe imaging noise, among which various bone pathologies are represented. Our system proves to be efficient; it achieves a clinically significant segmentation accuracy level (Dice Coefficient 98.2%).