Computerized　tumor　segmentation　on　breast　ultrasound　(BUS)　images　remains　a　challenging　task.　In　this　paper,　we　proposed　a　new　method　for　semi-automatic　tumor　segmentation　on　BUS　images　using　Gaussian　filtering,　histogram　equalization,　mean　shift,　and　graph　cuts.　The　only　interaction　required　was　to　select　two　diagonal　points　to　determine　a　region　of　interest　(ROI)　on　an　input　image.　The　ROI　image　was　shrunken　by　a　factor　of　2　using　bicubic　interpolation　to　reduce　computation　time.　The　shrunken　image　was　smoothed　by　a　Gaussian　filter　and　then　contrast-enhanced　by　histogram　equalization.　Next,　the　enhanced　image　was　filtered　by　pyramid　mean　shift　to　improve　homogeneity.　The　object　and　background　seeds　for　graph　cuts　were　automatically　generated　on　the　filtered　image.　Using　these　seeds,　the　filtered　image　was　then　segmented　by　graph　cuts　into　a　binary　image　containing　the　object　and　background.　Finally,　the　binary　image　was　expanded　by　a　factor　of　2　using　bicubic　interpolation,　and　the　expanded　image　was　processed　by　morphological　opening　and　closing　to　refine　the　tumor　contour.　The　method　was　implemented　with　OpenCV　2.4.3　and　Visual　Studio　2010　and　tested　for　38　BUS　images　with　benign　tumors　and　31　BUS　images　with　malignant　tumors　from　different　ultrasound　scanners.　Experimental　results　showed　that　our　method　had　a　true　positive　rate　(TP)　of　91.7%,　a　false　positive　(FP)　rate　of　11.9%,　and　a　similarity　(SI)　rate　of　85.6%.　The　mean　run　time　on　Intel　Core　2.66　GHz　CPU　and　4　GB　RAM　was　0.49　+/-　0.36　s.　The　experimental　results　indicate　that　the　proposed　method　may　be　useful　in　BUS　image　segmentation.