Three-dimensional　(3D)　liver　tumor　segmentation　from　Computed　Tomography　(CT)　images　is　a　prerequisite　for　computer-aided　diagnosis,　treatment　planning,　andmonitoring　of　liver　cancer.　Despitemany　years　of　research,　3D　liver　tumor　segmentation　remains　a　challenging　task.　In　this　paper,　an　efficient　semiautomatic　method　was　proposed　for　liver　tumor　segmentation　in　CT　volumes　based　on　improved　fuzzy　C-means　(FCM)　and　graph　cuts.　With　a　single　seed　point,　the　tumor　volume　of　interest　(VOI)　was　extracted　using　confidence　connected　region　growing　algorithmto　reduce　computational　cost.　Then,　initial　foreground/background　regions　were　labeled　automatically,　and　a　kernelized　FCM　with　spatial　information　was　incorporated　in　graph　cuts　segmentation　to　increase　segmentation　accuracy.　The　proposed　method　was　evaluated　on　the　public　clinical　dataset　(3Dircadb),　which　included　15　CT　volumes　consisting　of　various　sizes　of　liver　tumors.　We　achieved　an　average　volumetric　overlap　error　(VOE)　of　29.04%　and　Dice　similarity　coefficient　(DICE)　of　0.83,　with　an　average　processing　time　of　45　s　per　tumor.　The　experimental　results　showed　that　the　proposed　method　was　accurate　for　3D　liver　tumor　segmentation　with　a　reduction　of　processing　time.