To achieve detection of tiny defect in radiographic images with complex background, the segmentation method of tiny defects was studied under the conditions of strong noise and large gray gradient background. The visual attention model for radiographic testing image was proposed, and its realization method was analyzed in detail. The human visual attention mechanism was simulated. The line scanning strategy and selfadapting centralperipheral difference strategy was adopted. Based on the vision saliency, the feature map and the saliency map were achieved, and visual attention region was segmented from radiographic images with complex background. Each visual attention region was marked and ordered with visual saliency competition. According to the saliency threshold, the suspicious region was identified. So the image data to be processed was reduced and the interference was discharged from other parts of radiographic testing image. Then attention focuses of the suspicious region was used as the seed point. Based on region growing and visual saliency, a segmentation method for tiny target was introduced to accurately extract tiny defects in suspicious region image. In the experiment about complex radiographic testing image with more tiny target objects, each area containing unknown defect was accurately extracted. Segmentation for tiny target achieved good results. The accuracy rate was 96.1%，and it was 8% higher than that of the traditional method. The results prove the effectiveness and adaptability of the proposed method.