Abstract: In the Non Destructive Testing (NDT), it is dealt with the
detection of defects in metallic pieces especially for indus-
trial use. These defects are mainly due to manufacturing
errors or to welding processes. In this article we will fo-
cus on this second category of defects using segmentation
techniques applied to the welded joints. The segmentation
remains among the most difficult tasks in image processing,
especially in the case of noisy or low contrasted images
such as radiographic images of welds. In segmenting this
type of images, many researchers 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 of seg-
mentation of digitized radiographic images which is based
primarily 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 found much more simple to implement.
Abstract: X-ray radiography is one of the most used tech-
niques 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.
Abstract: The segmentation remains among the most difficult tasks in
image processing, especially in the case of noisy or low
contrasted images.
In the field of Non Destructive Testing (NDT), many
researchers used neural networks and fuzzy logic methods to
solve the problem of segmentation applied to radiographic
images to detect welding defects in metal welded joints.
Theses works showed interesting results in terms of visual
quality; however they involve methods requiring a complex
implementation.
In this article, we present a new method of segmentation of
digitized radiographic images. The image is first pre-
processed using histogram analysis and homomorphic
filtering for contrast enhancement. The weld bead is isolated
through a segmentation based on global thresholding while
defects detection used local thresholding. The proposed
method is revealed to be very efficient in terms of
implementation for comparable results with those obtained
on the basis of techniques used by other authors.
