This study investigates a novel CT/MR spine image fusion algorithm based on graph cuts. This algorithm allows physicians to visually assess corresponding soft tissue and bony detail on a single image eliminating mental alignment and correlation needed when both CT and MR images are required for diagnosis. We state the problem as a discrete multilabel optimization of an energy functional that balances the contributions of three competing terms: (1) a squared error, which encourages the solution to be similar to the MR input, with a preference to strong MR edges; (2) a squared error, which encourages the solution to be similar to the CT input, with a preference to strong CT edges; and (3) a prior, which favors smooth solutions by encouraging neighboring pixels to have similar fused-image values. We further introduce a transparency-labeling formulation, which significantly reduces the computational load. The proposed graph-cut fusion guarantees nearly global solutions, while avoiding the pix elation artifacts that affect standard wavelet-based methods. We report several quantitative evaluations/comparisons over 40 pairs of CT/MR images acquired from 20 patients, which demonstrate a very competitive performance in comparisons to the existing methods. We further discuss various case studies, and give a representative sample of the results.