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Conventional radiography is one of the common non-destructive testing which employs manual image interpretation. The

As one of the non-destructive testing (NDT), industrial

this crucial process dealing with images, image analysis and

the subject of interest in the eld of industrial radiography.This trend of the radiographic interpretation and evalua-

tion, lack of weld fusion, porosity, voids and others.Every discontinuity has its own shape and contributes todifferent level of severity to the weldment. According to

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Corresponding author. Tel.: +603 89250510x1194;

ASME Code Section 1 certain types of discontinuities areclassied as major and unacceptable. These unacceptablediscontinuities are called defects and subject to repair and

0963-8695/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ndteint.2007.05.004

fax: +603 89250907.

E-mail addresses: hazal@mint.gov.my (H. Yazid),

haniza.yazid@gmail.com (H. Yazid).radiography plays a vital role in detecting volumetricdiscontinuities present in the critical area of engineeringcomponent. The most common critical area is welded jointthat forms part of the structures. A variety of NDTmethods are available for the inspection of welded joint [1].The emerging trend of radiography as one of the NDTmethods is towards automated inspections [26]. This isfeasible as the information of the discontinuities is in theform of radiographic images. Interpretation and evaluationof the radiographic images are very important stages foracceptance of any engineering component. Realization of

tion method is aimed towards an automated inspection,which results in better consistence assessment. Basically, itrequires development of image enhancement and objectdetection technique to assist human interpreter in evaluat-ing the quality of welded joints.The purpose of the radiographic interpretation is to

reach a decision on the acceptability or rejection of thecomponent being assessed. This is based on the adoptedcode and standard in use, for example ASME Code Section1 [7]. There are many types of discontinuities described bythe standard, which includes cracks, lack of weld penetra-interpretation is very subjective and depends much on the inspector experience and working conditions. It is therefore useful to have

pattern recognition system in order to assist human interpreter in evaluating the quality of the radiographed sample, especially

radiographic image of welded joint. This paper describes a system to detect circular discontinuities that are present in the joints. The

system utilizes together two different algorithms, which are separability lter to identify the best object candidate and Circular Hough

Transform to detect the presence of circular shape. The result of the experiment shows a promising output in recognition of circular

discontinuities in a radiographic image. This is based on 81.82100% of radiography lm with successful circular detection by using

template movement of 10 pixels.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Non-destructive testing; Algorithm; Welded joints; Separability lter; Circular Hough Transform

1. Introduction pattern recognition system using a computer have becomeCircular discontinuities detCircular Hou

Hazal Yazida,, Haniza Yazidb, Mohd HMohamed Rizonb,

aIndustrial Technology Division, Block 34, Malaysian Institute for NbKolej Universiti Kejuruteraan Utara Malaysia (KUKU

Received 16 November 2006; received in revis

Available onli

Abstract2007) 594601

tion in welded joints usingh Transform

una, Shukri Mohda, A. Aziz Mohameda,haharudin Sayutia

ear Technology Research (MINT), Bangi, 43000 Kajang, Malaysia

), Jalan Kangar-Arau, 02600 Jejawi, Perlis, Malaysia

form 26 March 2007; accepted 25 May 2007

13 June 2007

www.elsevier.com/locate/ndteint

Once poor radiography image quality is identied anddigitized, preprocessing step is initialized. The image issubjected to contrast enhancement. In this work, 8-bitimage is used as an input image which carries 0255 graylevels. The image is in the form of PGM (Portable GrayMap) format and comprises 320 240 pixels. Every singlepixel in an image carries its own intensity. The distributionof the intensities of the pixels could be viewed statisticallyby plotting the intensity histogram. By manipulating thishistogram, the required intensity distribution or contrast isachieved. It is common to have narrow intensity distribu-tion, which causes poor image contrast, and this requiresrectication to acquire a wider intensity distribution. Inthis work, histogram equalization technique has been usedto improve the image contrast. By using this technique, it ispossible to acquire a wider intensity distribution ascompared to the original intensity distribution prole.One typical example is clearly shown in Fig. 2. Thereforethe image contrast is effectively enhanced. This technique isparticularly suitable for region-based feature extraction.Typical radiography image which has good contrast and

does not require histogram equalization is depicted inFig. 3. Poor radiography image quality that requireshistogram equalization is depicted in Fig. 4. The resultof histogram equalization on poor radiography image

ARTICLE IN PRESStern2. Methodology

A systematic procedure is established in analyzing theimage information and inferring patterns in such a waythat would yield the targeted object. The innovativeapproach in this procedure is to separate good imagequality and directly subject to separability lter and CHTwithout undergoing the preprocessing step. The otherimage still requires the preprocessing step for imageenhancement. Fig. 1 shows the ow procedure of thepattern recognition system. Initially the resulted radio-graphic lm is subjected to lm quality inspectionmanually. The inspection covers many criteria that includelm density, sensitivity, presence of artifacts and identica-tion. All the criteria have to be met as governed by theadopted standard in order for the radiographic lm to beaccepted for evaluation. Even though the radiographic lmis accepted for evaluation, it does not mean that the imageappeared on the lm is good in terms of contrast. A lm1.1. Industrial radiography

In industrial radiography, typical images are generatedfrom using two types of radiation source, namely X-rayand gamma ray. Both differ primarily in energy andpenetration power. X-ray suffers lower penetration powerand energy as compared to gamma ray. The advantage ofX-ray over gamma ray is that it has a wider energyspectrum while energy for gamma ray is discrete [9]. Thisenables X-ray image to acquire higher latitude and bettercontrast. In other words, for most of the weldedcomponents, good image quality is attainable throughrecommended radiographic practice, thus, making theconventional radiography interpretation feasible to becarried out in an automated manner.reexamination. Such defects are cracks, lack of weldpenetration and lack of weld fusion. Other than thatincluding rounded discontinuity is still depending on thesize, distribution and concentration as specied by thestandard.In this paper, the work is intensied in nding one

type of discontinuity that is circular and appeared asrounded image on the radiograph. The circular shape istreated as an object of detection. The approach is tooptimize separability lter technique and Circular HoughTransform (CHT) in order to obtain the result. The ltertechnique is used to recognize the best object candidatesand later identied by CHT. Similar approaches have beenused to detect coconut as circular object in coconut treeimage with prominent success rate by Haniza et al. [8].The use of CHT extends also to other research eldssuch as iris detection for face recognition and ngertipsdetection [9,10].

H. Yazid et al. / NDT&E Inwould suffer poor radiography image quality or contrastonce the requirement of the criteria is only met on aminimal basis. Therefore poor and good radiographyimage quality is identied before the lm is digitized.

2.1. Preprocessing

Contrast Enhancement

DefectSegmentation and Noise Reduction

Separability Filter

CircularHough Transform

Good Image

Digitization Digitization

Manual FilmQualityInspection

Radiographic Image

End result

Poor Image

Fig. 1. Flow procedure of the pattern recognition system.

ational 40 (2007) 594601 595quality is depicted in Fig. 5. The same result of histogramequalization is depicted in Figs. 6 and 7, respectively.

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H. Yazid et al. / NDT&E In5962.2. Defect segmentation

This step is crucial to distinguish between the object ofinterest and the background. The term segmentationimplies segmenting the object of interest or foreground

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Fig. 2. The effect of histogram equaliz

Fig. 3. Typical image, which has good contrast and does not require

histogram equalization.

Fig. 4. Typical image, which has poor contrast and requires histogram

equalization.from the background. Edge nding and mathematicalmorphology are used in this step. Edge-nding techniqueprovides detection of pixels that belong to the borders ofthe objects. As a result, the image contains only a collectionof edge pixels. The collection of edge pixels covers most ofour targeted object and also unwanted information ornoise. Unwanted information is reduced by using mathe-matical morphology. By doing this we will able to obtainedge pixel that mostly corresponds to the targetedobject. There are various edge-nding techniques availableand in this work Sobel edge detector has been used.This technique reveals thick edge as compared to othertechnique particularly Canny [10]. Sobel edge detector ispreferred due to its ability to reduce the chances of falseobject detection as compared to Canny. Canny reveals too

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ational 40 (2007) 594601many details, which in turn cause the CHT to give a wrong-targeted object. Edge information is very important andcomes into play when performing CHT step. Sobel edgedetector is a gradient method, which detects the edges bylooking for the maximum and minimum in the rstderivative of the image. Gradient is reected by thedifference in intensity from one pixel to the next. Sobeledge detector uses a pair of 3 3 convolution masks, oneestimating the gradient in the X-direction, Gx and the otherestimating the gradient in the Y-direction, Gy. The Sobelconvolution masks are shown in Fig. 8.The result of each convolution mask is combined to get

exact magnitude value using the formula

jGj Gx2 Gy2

p. (1)

The Sobel technique deals with six pixels and thus has abetter average over the neighboring pixels. This approachwith slight blurring can eliminate most noise found in theimage.Mathematical morphology is considered as region-lling

technique to provide the complete objects. This technique

is implemented by using closing and opening operation.First the closing operation is carried out followed byopening operation. The closing operation is able to ll insmall holes and removes much of the white pixel noise.This would yield a fairly clean image as compared toprevious image before the operation. Then opening opera-tion takes place by opening small gaps or spaces betweentouching objects in an image. The opening operation canseparate objects that are connected in a binary image. Bothoperations generate a certain amount of smoothing on anobject contour given a smooth structuring element.

2.3. Separability filter

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Fig. 5. Image as in Fig. 4 after histogram equalization.

Fig. 6. Another image, which has poor contrast and requires histogram

equalization.

Fig. 7. Image as in Fig. 6 after histogram equalization.

Fig. 8. Sobel convolution masks.

H. Yazid et al. / NDT&E International 40 (2007) 594601 597After completing segmentation step, the image issubjected to feature extraction step by using separabilitylter technique. This technique is a template-based methodand was proposed by Fukui [11]. This technique employs atemplate of Fig. 9 by placing the template at each point(xi, yi) and then computation on the separability betweenthe two regions R1 and R2 in the template with size r iscarried out.Eq. (4) is used to measure the separability between the

two regions. r is treated as a xed parameter based onmaximum acceptable circular defect size specied by thestandard. r is xed at 3, 4 and 5 pixels that correspondapproximately from 0.75 to 1.5mm defect radius.

A XN

i1Ixi; yi Pm2, (2)

B n1P1 Pm2 n2P2 Pm2, (3)

Z BA, (4)

where nk (k 1,2) is the number of pixels in Rk;N n1 n2; Pk (k 1,2) the average intensity in Rk; Pmthe average intensity in the union of R1 and R2; and Ixi; yithe intensity values of pixels xi; yi in the union of R1and R2.Fig. 9. Circle template for object candidate detection.

The points (xi,yi) obtained after the opening morphologyin the segmentation process is used as the input to theseparability lter. The range of diameter for the R1 is set to{2rL,y,2rU} and in this work rL is 3 pixels and rU is 5pixels. Diameter of R2 region is obtained after consideringthe gap of 0.5r. The template of Fig. 9 will move every xpixels from center to the left and right and y pixels fromcenter to upwards and downwards as shown in Fig. 10. Thecircles that give the local maxima of the separability Z areselected as object candidates.In this work; x 10 pixels, y 10 pixels.

2.4. Circular Hough Transform

Circular Hough Transform or CHT is used as pattern

information is used to reduce the ranges of parameters andthe complexity of the voting operation. The center of acircle must be on the straight line passing through the edgepoint (x, y) in a direction given by the gradient value at theedge point. The use of edge orientation information isproposed by Kimme et al. [13] and this limits the possiblepositions of the center for each edge point. This methodapplies the use of an arc plotted perpendicular to the edgeorientation at a distance r from the edge point.Each edge point contributes a circle of radius r to an

output accumulator or parameter space. The outputaccumulator space has a peak where these contributedcircles overlap at the center of the original circle. The peakpoint is projected back to the image space as (x0, y0, r)which reveal the nal detected circle object. This circleconsists of many edge points at its perimeter.Hough transform and its modied version offer several

advantages. First the possibility of parallel processing sinceeach image point is treated independently and thealgorithm is able to work even when the shape is occluded.Secondly, the method is robust even in the presence ofnoise. Noisy image points are very improbable tocontribute to a peak in the parameter space. Thirdly,the method is able to detect different instances of the

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Fig. 12. Parameter space.

H. Yazid et al. / NDT&E Intern598classication to identify the targeted object. This is the laststep need to be carried out before obtaining the end result.Hough transform is a widespread technique in imageanalysis. It was rst introduced by Paul Hough in 1962. Itwas rst used to detect straight lines and later extended tocater to other parametric shapes such as circular, ellipsesand nally being generalized to cater to any parametricshape. CHT was proposed by Duda et al. [12]. It has beenrecognized as a robust technique for curve detection.CHT is used to transform a set of feature points in the

image space into a set of accumulated votes in a parameterspace. Therefore the difculties in global detection problemin the image space are reduced to peak nding in aparameter space. The image space and parameter space areshown in Figs. 11 and 12, respectively. In this work, theinteresting image points are those that belong to a circlecharacterized by its center coordinates (x0, y0) and itsradius r can be represented by

x x02 y y02 r2. (5)This equation indicates that a point (x, y) of the image

space can be mapped to a cone surface in the (x0, y0, r)parameter space, which represents all possible circlespassing through (x, y). In this work, the edge orientationFig. 10. Template movement.Fig. 11. Image space.

ational 40 (2007) 594601desired shape at the same time, depending on the numberof peaks, which are considered in the parameter space.

The advantages over other techniques make it an attractivemethod for circle detection in many research elds.

3. Results and discussions

The system was tested by evaluating 11 radiographyimages. The radius of the object as the input parameter wasset to 3, 4 and 5 pixels, which correspond to approximately1.53.0mm defect diameter size. Table 1 shows the results

image contrast after histogram equalization eases into thefollowing process ow of object detection. Additionally, inthis work, the effect of mathematical morphology eases the

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Fig. 13. Typical image after applying Canny edge detector for image in

Fig. 3.

Fig. 14. Typical image after applying Sobel edge detector for image in

Fig. 3.

Fig. 15. Effect of mathematical morphology. Image (a) indicates image

before being subjected to mathematical morphology. Image (b) indicates

image after being subjected to mathematical morphology.

H. Yazid et al. / NDT&E Intern1 100

2 100

3 90.9of the experiment.First and second detections successfully detect the

present of circular discontinuities for all the radiographyimages. Typical successful detection is depicted in Fig. 17.The same image before the detection is depicted inFig. 16. Third and fourth detections also give higherpercentage of successful detection with 90.9% and 81.82%,respectively.The reduction in successful or correct detection is due to

the present of ambiguous object detected by the system asin Fig. 19. Image before detection is depicted in Fig. 18. Itis not surprising for CHT to give false detection since thepresence of ambiguous object does exist after applyingSobel edge detector as in Fig. 14. Thus the performanceof the proposed system is far better if the ambiguousobject outside the region of interest is eliminated afterapplying the edge detector. Fifth detection gives 54.55% ofsuccessful detection. Almost half out of the total lm isable to give correct detection. Besides the factor ofambiguous object, this is also an indicator that all thecircular discontinuities that are present in the area ofinterest have been detected by the system and the rest of thedetection become false detection in order for a computerprogram to complete.In this work, Sobel edge detector has been selected.

This type of edge detector would reduce the amount ofambiguous object and reveal cleaner image as compared toCanny edge detector. Both of the edge detectors aredepicted in Figs. 13 and 14 for comparison. Some of theimage does not require histogram equalization due to thefact that it has already a good image quality and contrast.This typical image is depicted in Fig. 3. On the contrary,poor image quality and contrast image are requiredto undergo histogram equalization. This typical imageis depicted in Fig. 4. The same image after applyinghistogram equalization is depicted in Fig. 5. The improved

Table 1

Success in circular detection

No. of success in circular

detection

Percentage of lm according to

success no. of detection (%)4 81.82

5 54.55ational 40 (2007) 594601 599burden of CHT by removing pixels or unwanted informa-tion and thus leaves only potential pixels for CHT to do the

with Sobel edge detector, the circular object is easily

[5] Builtjes JH, Rose P, Daum W. Automatic evaluation of weld

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Fig. 17. Image as in Fig. 16, after detection.

Fig. 16. Image before detection.

Fig. 18. Image before detection.

Fig. 19. Image as in Fig. 18, after detection.

H. Yazid et al. / NDT&E Intern600radiographs by digital image processing. In: Halmshaw R, editor.

X-ray real time radiography and image processing, Proceedings of

symposium, Newbury, Berkshire; 1988. p. 6372.

[6] Munro JJ, et al. Weld inspection by real time radioscopy. Mater Eval

1987;45(11):13039.way to avoid the presence of ambiguous object and ndinga technique to accurately determine the size of the detectedcircular object.

Acknowledgments

The authors wish to thank MINTS NDT group andKhairiah Yazid for providing the image data andSheriffah Noor Khamseah for proofreading the paper.The excellent support from MINT and KUKUM through-out the research is highly appreciated.

References

[1] Edward GR. Inspection of welded joints. ASM handbook, welding,

brazing and soldering, vol. 6. Materials Park, OH: ASM Interna-

tional; 1993. p. 10818.

[2] Boerner H, Strecker H. Automated X-ray inspection of aluminium

castings. IEEE Trans Pattern Anal Mach Intell 1988;PAMI-10(1):

7991.

[3] Demandt K, Hansen LK. Real-time X-ray system with fully

automated defect detection and quality classication. In: Halmshaw

R, editor. X-ray real time radiography and image processing,

Proceedings of symposium, Newbury, Berkshire; 1988. p. 96119.

[4] Gayer A, Sayer A, Shiloh A. Automatic recognition of welding

defects in real time radiography. NDT Int 1990;23(3):1316.detected by separability lter and CHT technique. Addi-tionally, this work shows good image contrast orquality could avoid image enhancement in the preproces-sing step of the pattern recognition ow procedure. Eventhough the proposed system is efcient in circulardetection, care has to be taken in the edge detector stageto avoid ambiguous object that could lead to falsedetections. Further research will be focused on nding aanalysis. This is depicted in Figs. 1519. Every single stepin the process ow is essential especially before theapplication of separability lter and CHT technique.

4. Conclusions

The work could be considered as an evidence of theapplicability of the separability lter and CHT in thedetection of circular object in the radiography image.A 100% of lm was found to have rst and secondcorrect circular detections by the proposed system.Third and fourth detections also give higher lm percen-tage of successful detection with 90.9% and 81.82%,respectively. This indicates that the system is suitablefor the use of circular detection in the welding area.By having a proper image quality and contrast coupled

ational 40 (2007) 594601[7] American Society of Mechanical Engineers (ASME), Section 1; 1998,

p. 204.

[8] Yazid H, et al. Object detection using circular Hough transform. Am

J Appl Sci 2005;2(12):16069.

[9] DOrazio T, Guaragnella C, Leo M, Distante A. A new algorithm for

ball recognition using circle Hough transform and neural classier.

Pattern Recognition 2004;37:393408.

[10] Kawaguchi T, Hidaka D, Rizon M. In: Proceedings of IEEE ICRP,

vol. 1; 2000, p. 4952.

[11] Fukui K, Yamaguchi O. Facial feature points extraction method

based on combination of shape extraction and pattern matching.

Trans IEICE Jpn J80-D-II 1997;8:21707.

[12] Duda RO, Hart PE. Use of the Hough transformation to detect lines

and curves in picture. Commun ACM 1972;115.

[13] Kimme C, Ballard D, Sklansky J. Finding circles by an array of

accumulators. Commun ACM 1975;18(2):1202.

ARTICLE IN PRESSH. Yazid et al. / NDT&E International 40 (2007) 594601 601

Circular discontinuities detection in welded joints using Circular Hough TransformIntroductionIndustrial radiography

MethodologyPreprocessingDefect segmentationSeparability filterCircular Hough Transform

Results and discussionsConclusionsAcknowledgmentsReferences