In　order　to　improve　the　accuracy　and　efficiency　of　weld　defect　segmentation　in　automatic　radiographic　nondestructive　testing　and　evaluation(NDT&E),　an　effective　weld　defect　segmentation　algorithm　based　on　flooding　has　been　developed,　which　has　the　self-adaptive　characteristics.　Firstly,　the　defect＇s　feature　points　are　extracted　from　the　scale　space　of　radiographic　films.　Based　on　the　information　of　defect　points,　the　seed　points　and　seed　domains　of　defect　discrimination　are　adaptively　determined,　in　which　the　defect　segmentation　seed　will　be　searched.　Then,　aiming　at　the　sparsity　of　weld　defects　and　canyon　characteristics　of　3D　topographic　map　of　defect　regions,　the　drip-watering　and　water　flooding　have　been　used　for　reference.　The　flooding　is　carried　out　by　using　line-flooding　algorithm,　in　which　water　starts　from　defect　seed　points　and　flows　to　the　neighbor　regions　in　order.　On　the　basis　of　the　flooding-area　change　and　flooding-level　ascending　velocity,　the　defect　segmentation　threshold　values　are　determined　and　the　weld　defects　also　are　segmented　from　the　radiographic　films.　At　last,　the　comparative　experiments　have　been　carried　out　to　compare　the　proposed　algorithm　with　the　watershed　segmentation　algorithm　and　background　subtraction　segmentation　algorithm.　And　the　experiment　results　confirm　that　the　proposed　algorithm　obviously　improves　the　accuracy　and　efficiency　of　weld　defect＇s　segmentation.