X-ray radiography is one of the most used techniques in the Non Destructive Testing (NDT). It allows the detection of weld defects the most dangerous for the weld's integrity. Because X-ray images of welds are noisy and low contrasted, it is difficult to detect weld defects inside. The goal of this paper is to segment the defects in X-ray images. However, the segmentation remains among the most difficult tasks in image processing, especially in the case of noisy or low contrasted images. Many researchers used neural networks, fuzzy logic methods or SVM-based methods to segment this type of images. The results are impressive; however they require a complex implementation and are time consuming because of learning stage. In this work, we present a new method of segmentation of digitized radiographic images of welds which is based on thresholding techniques and compare it with a multiple thresholding and Support Vector Machines based method. We obtained the same results in terms of visual segmentation quality, but our algorithm is faster.
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