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A new co-operative segmentation method applied to X-ray imagesYacine KABIR,Redouane DRAI
signal and image processing Laboratory
Centre de soudage et contrôle, Route de Dely Ibrahim, B.P.64, Chéraga, Algeria
Tel.: (213) (2) 36 18 54 to 56, Tel/Fax: (213) (2) 36 18 50,
Email : firstname.lastname@example.org,email@example.com
Signal and image processing Laboratory
Electronic Institute / University of Blida, Route de Soumaa, B. P.270, Blida, Algeria
Tel.: (213) (3) 41 58 53, Fax: (213) (3) 41 78 13,
However there is no unique segmentation, for each application, it is necessary to choice the appropriate technique, with the adapted parameters. This study has permitted to conclude that we cannot favour one method in relation to the other one. The basic idea is to use the two approaches in a single process that exploit the advantages of one technique to overcome the drawbacks of the other one. In this paper we propose a new method by combining estimation, enhancement, edge detection and a region growing in a an intelligent process.
keywords: defects, edge detection, filtering, region growing, segmentation, X-rays image.
We present in this paper, the result of a welding defects characterisation technique of welded joints radiogram images. To this end, the image will be segmented in order to put failures in obviousness. Image segmentation is a fundamental stage in any artificial vision system. It allows producing a compact description that can be characterised by edges or by homogeneous regions. Image edges represent a very rich information and have been extensively studied. The "region" approach is complementary to the "contour" approach, however it does not provide the same results. We have studied and tested several segmentation techniques based on the two approaches. There is no unique segmentation, for each application, it's necessary to choice the appropriate technique, with the adapted parameters. Indeed each technique have its advantages, drawbacks and its limitations. The aim of each one is closely related to the application in question.
A new tendency consist of combining these two approaches in order to obtain a robust segmentation by exploiting the advantages of one method to reduce the drawbacks of the other one. We will present a new method by combining estimation, enhancement, edge detection and a region growing in a an intelligent process. A quantitative analysis to extract some relative geometrical parameters will be applied.
To the sight of these characteristics, a first classification will be possible. We proceed as follow:
|Fig 1: Flow chart of the NDT project|
Step 2 and 3 are executed automatically by the new intelligent co-operative segmenter proposed.
Several practical results will be presented and commented.
This study describes one stage of a software tools realisation, which facilitate and improve the task of the expert for x-ray image interpretation and decision-making. We are interested by the first four stages of this flow chart. Defects intervening in pieces are listed by official norms. For segmentation needs, we have divided the set of defects in two categories, volumetric and linear defects. A defect is considered as linear if its width is twice inferior to its size, all the rest are considered as volumetric defects.
Due to the great number of disruptive factors, the visual information provided by an X or gamma ray image is complex. The processed images are characterised by three particular phenomena:
The importance of these phenomena varies from one image to an other, according to the nature of the metal, the thickness of the piece and the type of radiation employed. For all the reasons described above, we have to perform some operations, described in section 3:
Pieces and defects nature
Inspected pieces are of variable forms and thickness. Risky zones are those submitted to important physical constraints. Two types of pieces are mainly controlled:
welds, levelled or not levelled;
cast pieces, such as elbows.
Defects intervening in pieces are listed by official norms. For segmentation needs, we have divided the set of defects in two categories, volumetric and linear defects. A defect is considered as linear if its width is twice inferior to the size of the grain, all the rest are considered as volumetric defects.
Edge detection, when we are interested by object shapes ;
Region segmentation, when we are interested by the image element contents.
The study of the advantages and drawbacks of the different segmentation methods, leads us to the following concluding remarks :
A comparison between the two approaches (edge and region segmentation) has shown that neither edge or region segmentation can provide alone an ideal segmentation. The future of image analysis is probably in the co-operation between these two approaches. A co-operation technique permit to exploit the advantages of each method in order to reduce the drawback effects of the other one.
The segmentation method is performed according to the following stages:
Construction of a preliminary edge map (with a robust operator), allowing to obtain high precision on image objects border;
The co-operation process begin by an edge detection ; the obtained edges are well localized, and corresponds precisely to the real region borders. The segmentation quality depends largely on the region boundaries detected. The edge operator can:
Small edge elimination (size varies with the application in question)
The edge detection operator is sensitive to all gray level variation, it can present wrong responses in textured or noisy zones; in fact, this operator don't take in consideration local characteristics; apart from this fact, sometimes, threshold values are not valid for the whole image. It's then very important to eliminate these false small edge chains, while respecting the type of picture to segment, and the nature of mannered objects. At this step, there's a risk to eliminate true edge chains, and in spite of all, some small chains without significance are still not suppressed because of the threhold values used.
A growing region segmentation, detects better the content of objects in an image, because of the utilization of homogeneity criteria. These criteria permit the detect zones having the same texture attributes. It will be therefore possible to confirm the true contours due to a discontinuity between two distinct adjacent regions, and to eliminate the false edges, due to a sensitivity to micro-textures, to a degraded gray level, or to a shade, etc... . The growing process doesn't evolve beyond contours.
For the aggregation process, we will use a double criteria; the growth is stopped when the pixel candidate to the aggregation:
A pixel verifying the criteria, and coinciding with a contour, is merged with region in progress , in an ulterior stage.
Because of the edge constraint that the process of region growing undergoes; small regions having the measurements of small edge chains will be created
fusion of small regions, whose sizes, can be adjusted by the operator, or fixed according to the type of the image being treated
The region growing phase, generate a big number of small regions because of the choice of parameters used. The edge constraint in the last step, can also give birth, to small isolated regions (figure 2).
|Fig 2: Creation of small regions because of the edge constraint, The region growing process bypass the small edge chains and consequently create small regions, that we have to suppress|
A region is identified as belonging to this false region type, if all its points coincide with edge points. Therefore, We have two types of small regions to be merged:
Verification of the common Boundary between each pair of adjacent regions, in order to confirm their dissimilarity (fusion of over-segmented regions)
Over-segmented regions are generally owed to an insufficient illumination, to shades, or to degraded of color or light. Several simple and composed criteria are used to operate this verification. For example, two adjacent regions are merged if:
Elimination of small chains situated far from the boundary of any region;
It's possible that some false chains persist; they are suppressed if they are completely drowned in homogeneous regions,
|Fig 3: Closing contour example by par prolongation on region boundary|
contour closing of the edge map controlled by the region segmentation map;
The edge map presents a lot of discontinuous segments, that must be closed. To correct these imperfections, an operation of closing contour is done, the process of closing is performed by prolongation on region boundaries (boundary following), when edges are sufficiently close to this boundary , otherwise the following operation is made according to the image gradient (figure 3).
Extraction of connected components from the edge map
In the connected components image, the closed contours delimit the different regions , each one have a unique label. These components constitute in fact, the regions whose attributes can be easily calculated from the original image
Evaluation of the compatibility between the two maps, edge and region, using dissimilarity measures
Geometrical features extraction
We have implemented all segmentation techniques described above, in an interactive software. The operator can chose and adapt the appropriate segmentation technique with the desired parameters. Some geometrical features can be extracted using a contour following such as length, width, surface, form, median axes of the selected defect. The selection is easy and can be done interactively using the mouse.
Example :linear or volumic defect
The geometrical measurements previously extracted help the making decision system to decide for example whether the defect is linear or not. This defect discrimination into two categories is considered as a first attempt for defect classification. To this end we define a linearity ratio (RL). RL =Length / width.
If RL is equal or near to "1", the defect is volumic ( ex: porosity), , otherwise it is a linear defect ( ex: cracks, lack of penetration).
|Fig 4: Application of the co-operative segmentation method on x-rays films Presenting several defects. a1) inclusion defect a2) longitudinal crack a3) porosity a4) lack of penetration bi) region maps (i=1,2,34, ) ci) border maps (i=1,2,3,4) dj) edge maps (i=1,2,3,4)|
With the proposed co-operative segmentation, we can remark on figure 4 that problems encountered with segmentation methods based only on one approach (edges or regions) are partially resolved, we have a good localization on region borders thanks to the edge constraint introduced on the region growing process; contours are well closed because of the edge prolongation on region borders. The precision on defects form is enhanced by the use of a robust edge detection. The defect characterisation is therefore made efficiently . In other hand, the region growing segmentation process is stopped at the right location even if the merging criteria parameters are not severe.
According to the post-processing carried out, the two results can be used. A saving structure of the result was created in order to enable a suitable exploitation.
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