Dynamic　contrast-enhanced　magnetic　resonance　imaging　(DCE-MRI)　can　show　subtle　lesion　morphology,　improve　the　display　of　lesion　definitions,　and　objectively　reflect　the　blood　supply　of　breast　tumors;　it　can　also　reflect　different　strengthening　patterns　of　normal　tissues　and　lesion　areas　after　medical　tracer　injection.　DCE-MRI　has　become　an　important　basis　for　the　clinical　diagnosis　of　breast　cancer.　To　DCE-MRI　data　acquired　from　several　hospitals　across　multiple　provinces,　a　series　of　in-silico　computational　methods　were　applied　for　lesion　segmentation　and　identification　of　breast　tumor　in　this　paper.　The　image　segmentation　methods　include　Otsu　segmentation　of　subtraction　images,　signal-interference-ratio　segmentation　method　and　an　improved　variational　level　set　method,　each　has　its　own　application　scope.　After　that,　the　distribution　of　benign　and　malignant　in　lesion　region　is　identified　based　on　three-time-point　theory.　From　the　experiment,　the　analysis　of　DCE-MRI　data　of　breast　tumor　can　show　the　distribution　of　benign　and　malignant　in　lesion　region,　provide　a　great　help　for　clinicians　to　diagnose　breast　cancer　more　expediently　and　lay　a　basis　for　medical　diagnosis　and　treatment　planning.　©　2014,　Shanghai　Jiaotong　University　and　Springer-Verlag　Berlin　Heidelberg.