Welding Defect Detection by Segmentation of Radiographic Images

Detection of defects in metallic pieces is an important application in the field of non-destructive testing (NDT), particularly in an industrial setting. These defects are mainly due to manufacturing errors or welding processes. In this article we will focus on this second category of defects using segmentation techniques applied to the welded joints. Segmentation is one of the most difficult tasks in image processing, particularly in the case of noisy or low contrast images such as radiographic images of welds. In segmenting this type of image, many researchers have used neural networks and fuzzy logic methods. The results are impressive, however the methods require a complex implementation and are time consuming. In this work, we propose a new method for segmenting digitized radiographic images which is based on histogram analysis, contrast enhancement and image thresholding. Computing time is optimized by using integral images to calculate the local thresholds. Although the method gives comparable results to those obtained by previous methods in terms of visual segmentation quality, it is significantly simpler to implement.