Accurate　segmentation　of　liver　from　abdominal　CT　scans　is　critical　for　computer-assisted　diagnosis　and　therapy.　Despite　many　years　of　research,　automatic　liver　segmentation　remains　a　challenging　task.　In　this　paper,　a　novel　method　was　proposed　for　automatic　delineation　of　liver　on　CT　volume　images　using　supervoxel-based　graph　cuts.　To　extract　the　liver　volume　of　interest　(VOI),　the　region　of　abdomen　was　firstly　determined　based　on　maximum　intensity　projection　(MIP)　and　thresholding　methods.　Then,　the　patient-specific　liver　VOI　was　extracted　from　the　region　of　abdomen　by　using　a　histogram-based　adaptive　thresholding　method　and　morphological　operations.　The　supervoxels　of　the　liver　VOI　were　generated　using　the　simple　linear　iterative　clustering　(SLIC)　method.　The　foreground/background　seeds　for　graph　cuts　were　generated　on　the　largest　liver　slice,　and　the　graph　cuts　algorithm　was　applied　to　the　VOI　supervoxels.　Thirty　abdominal　CT　images　were　used　to　evaluate　the　accuracy　and　efficiency　of　the　proposed　algorithm.　Experimental　results　show　that　the　proposed　method　can　detect　the　liver　accurately　with　significant　reduction　of　processing　time,　especially　when　dealing　with　diseased　liver　cases.