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Energy 68 (2014) 385e398

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Energy

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Automatic X-ray inspection for escaped coated particles in sphericalfuel elements of high temperature gas-cooled reactor

Min Yang a,*, Qi Liu a, Hongsheng Zhao b,**, Ziqiang Li b, Bing Liu b, Xingdong Li c,Fanyong Meng d

a School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, Chinab Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinacNational Institute of Metrology, Beijing 100013, Chinad State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China

a r t i c l e i n f o

Article history:Received 13 September 2013Received in revised form24 January 2014Accepted 20 February 2014Available online 21 March 2014

Keywords:Spherical fuel elementsHigh-temperature gas-cooled reactorsDigital radiographyAutomatic recognition

* Corresponding author. School of Mechanical EBeijing University of Aeronautics and Astronautics, XuDistrict, Beijing 100191, China. Tel./fax: þ86 10 82339** Corresponding author. Institute of Nuclear anTsinghua University, Beijing 100084, China. Tel.: þ86

E-mail addresses: minyang.ndt@263.net, minyanghshzhao@tsinghua.edu.cn (H. Zhao).

http://dx.doi.org/10.1016/j.energy.2014.02.0760360-5442/� 2014 Published by Elsevier Ltd.

a b s t r a c t

As a core unit of HTGRs (high-temperature gas-cooled reactors), the quality of spherical fuel elements isdirectly related to the safety and reliability of HTGRs. In line with the design and performance re-quirements of the spherical fuel elements, no coated fuel particles are permitted to enter the fuel-freezone of a spherical fuel element. For fast and accurate detection of escaped coated fuel particles, X-rayDR (digital radiography) imaging with a step-by-step circular scanning trajectory was adopted for Chi-nese 10 MW HTGRs. The scanning parameters dominating the volume of the blind zones were optimizedto ensure the missing detection of the escaped coated fuel particles is as low as possible. We proposed adynamic calibration method for tracking the projection of the fuel-free zone accurately, instead of using afuel-free zone mask of fixed size and position. After the projection data in the fuel-free zone wereextracted, image and graphic processing methods were combined for automatic recognition of escapedcoated fuel particles, and some practical inspection results were presented.

� 2014 Published by Elsevier Ltd.

1. Introduction

As the most populous nation on earth, China’s rapid growth andindustrialization have fueled an urgent need for increased powergeneration. As a vital source of electricity, nuclear power will helpto reduce China’s dependence on coal, natural gas, and oil to driveits rapid growth and modernization [1e4]. The HTGR (high-tem-perature gas-cooled reactor) is generally recognized one of the bestcandidates for generation IV nuclear power system [5]. InDecember, 2000, the Chinese 10 MW high temperature gas-cooledreactor (HTR-10) reached its first criticality, and realized full poweroperation at the beginning of 2003 [6]. The HTR-10 is a modularpebble-bed-type high temperature gas-cooled reactor whosespherical fuel element is based on that of the HTR-Module of

ngineering and Automation,eyuan Road, No. 37, Haidian481.d New Energy Technology,13 621096678 (mobile)..ndt@buaa.edu.cn (M. Yang),

Siemens/Interatom in Germany. According to the reactor physicsdesign of the HTR-10, a total of 27,000 spherical fuel elements wereloaded in the core of the HTR-10 under the equilibrium state.

As a core unit of HTGR, spherical fuel elements have beenresearched and developed for more than 40 years in China [7]. Fig.1shows the typical structure of a spherical fuel element, which canbe divided into fuel-free zone and fuel zone. Fuel-free zone is aspherical shell of pure graphite matrix with 5-mm thickness, whichserves as a neutron moderator as well as a heat conductor trans-mitting the power generation to the coolant. Being surrounded bythe fuel-free zone, fuel-zone is a ball of 50-mm in diameter, inwhich the fissile material in the form of coated fuel particles isembedded in a matrix of graphite material. Each spherical fuelelement of 60 mm diameter contains about 8300 coated fuel par-ticles, and each coated fuel particle is 0.92 mm in diameter.

To insure the safety and reliable operation of the HTGR, thefabrication of a spherical fuel element must obey strictmanufacturing and quality control guidelines. In line with thedesign and performance requirements, the fuel-free zone is a puregraphite matrix of 5 mm thickness and no defects are permitted toexist in it. In particulars any coated fuel particles in the fuel-freezone will pose a threat to the safety and reliability of the fuel

Fig. 1. Structure of a spherical fuel element.

M. Yang et al. / Energy 68 (2014) 385e398386

elements [8,9]. Unfortunately, during the manufacture of a spher-ical fuel element, especially in the press line, some leakage ofcoated fuel particles and mixing with the outer pure graphite ma-trix is inevitable. According to a quality testing report from the INET(Institute of Nuclear Energy Technology), Tsinghua University, 44batches, a total of about 20,540 spherical fuel elements wereinspected. The result was that 99% of the inspected spherical fuelelements were qualified, and the remaining 1% were disqualifiedbecause of escaped coated fuel particles in the fuel-free zone [10].Therefore, an efficient and accurate NDT (non-destructive testing)method is necessary for reliable detection of escaped coated fuelparticles.

With the existing NDT methods for fuel elements inspection, X-ray imaging is an indispensable choice and plays an important rolein the quality control of fuel elements. Among the reported re-searches about X-ray inspection for spherical fuel elements, animportant one is micro-focus X-ray imaging used to obtain phasecontrast DR (digital radiography) image of a coated fuel particle.With this method, W. K. Kim performed nondestructive measure-ment of the coating layer thickness for simulated TRISO(tristructural-isotropic)-coated fuel particles [11]. M. Naghe-dolfeizi adopted X-ray fluorescence micro-tomography to measurethe trace elements spatial distribution in a TRISO SiC shell [12]. Forthe macroscopic space distribution visualization and analysis ofcoated fuel particles, CT (computed tomography) imaging isregarded as a highly effective and more accurate approach because,by this means, all cross-sectional slices of a spherical fuel elementcan be reconstructed, and consequently the space distribution ofcoated fuel particles, including the escaped coated fuel particles areeasy to identify. W. K. Kim used X-ray CT to generated 3D volumedata of simulated cylindrical compacts for HTGR, then, the weightdistribution as well as the number of kernels in a compact werecalculated [13]. E. H. Lehmann, P. Vontobel, and A. Hermann appliedneutron CT to investigate 3D distribution of the coated fuel parti-cles in the graphite matrix, in order to determine their uniformityand the fuel sphere’s content of fissile material [14]. However, forCT reconstruction of a spherical fuel element, metal artifact is themajor problemwhich is notwell solved so far. This kind of artifact iscaused by the large density difference between the uranium kerneland the graphite matrix of a spherical fuel element [15e19].Furthermore, CT scanning and image reconstruction are time-consuming, usually at least more than 10 minutes are needed forone inspection [20]. Thus, until now, X-ray CT inspection still staysin process and quality analysis stage and is not suitable for batchtest on the production line.

In our research, the aim is to realize fast automatic testing forescaped coated fuel particles in spherical fuel elements of HTGRs.As another important branch of X-ray imaging technology, DR

method can provide transmission projection of a spherical fuelelement in a short time with a cone-beam X-ray and a flat-paneldetector. This method is easily implemented and has been suc-cessfully realized in many engineering inspections, such as weldingdefects real-time radiography [21], on-line-inspection of interiorassembly structures of complex products [22], and other dynamicimaging applications. When using DR method for automatic andfast inspection of escaped coated fuel particles, the followingproblems we will face.

a) Being different from CT imaging, DR is the integration of an ob-ject’s 3D-support along the transmitting direction of X-rays.Consequently, overlapping of projection information is unable toavoid, resulting in blind zoneswhere projections of some escapedcoated fuel particles mix with projection of the fuel zone. How todesign the scanningways and optimize its parameters to decreasethe blind zone, and finally to ensure all the escaped coated fuelparticles can be detected, is a new topic in our research.

b) Before recognizing whether there are escaped coated fuelparticles in the fuel-free zone, the projection of the fuel-freezone must be calibrated in advance. However, due to shapeuniformities of the finished fuel elements and the instability oftransport and rotation motion, the fuel-free zone on eachprojection image is not the same, making it necessary tocalibrate the fuel-free zone dynamically to ensure the recog-nition accuracy. According to the previous studies about DRimaging system calibration, most of them are based on thestatic images of dedicated phantoms and are difficult to beimplemented dynamically. For example, Y. Cho et al. devel-oped an analytic algorithm to estimate the geometrical pa-rameters of the scanning system by using a phantomconsisting of 24 steel ball bearings [23]. S. G. Axevedo et al.used a specific straight metal wire to calibrate the rotationcenter of the scanning system [24]. In addition, according to G.Wang’s work, they developed a method for automatic identi-fication of welding defects in radiographic images [25]. Buttheir recognition region was the whole image and no sub-region of interest was calibrated.

c) After the projection data in the fuel-free-zone are obtained, it isdesirable to develop a computer identification system to in-crease the objectivity, consistency, accuracy, and efficiency ofonline inspection. To the best of our knowledge, one of the mostsuccessfully realized automatic inspections based on radio-graphic images is welding defects identification. Liao and Ni [26]proposed a methodology for the extraction of welds from digi-tized radiographic images. The method was based on theobservation that the intensities of pixels in the weld areadistribute more as a Gaussian distribution than other areas in

M. Yang et al. / Energy 68 (2014) 385e398 387

the image. Daum et al. [27] proposed a defect segmentationalgorithm based on a background subtraction algorithm. Thisalgorithm was proved effective regardless the defect types. Butit had difficulties in detecting small defect regions (4e6 pixels).In our view, an automatic recognition method of escaped coatedfuel particles should include two major functions: noisereduction and segmenting object from background. Imagedenoising and segmentation are fundamental problems in thefield of image processing and computer vision, and there are alarge number of algorithms worthy of reference. Such as NL-means (non-local means) denoising [28], PDE (partial differen-tial equation) based smoothing and segmentation [29], Bayesiansegmentation and curve evolution based segmentation [30,31],and so on. In our research, considering inspection efficiency andcomplexity of the algorithms, we adopted adaptive weightedmedian filter [32] to reduce image noise, and Ostu histogram-shape-based image thresholding to get segmented image [33],then, morphologic operation was performed to extract theescaped coated fuel particles’ information.

In this paper, we first present a systematic introduction to thedevelopment of the spherical fuel elements’ quality inspectionsystem in Section 2. Then, we discuss how to design the scanningparameters to decrease the blind zones so as to realize detectingescaped coated fuel particles as accurately as possible, when aspherical fuel element is scanned in a step-by-step circular trajec-tory as described in Section 3. And a dynamic calibration methodfor the fuel-free zone is proposed in Section 4. In Section 5, image-processing and recognition methods are presented. Some practicalinspection results are given in Section 6. Finally, the conclusion isdrawn in the last section.

2. Automatic radiographic testing system of spherical fuelelements

In our research, we developed a DR imaging systemwith whichwe automatically pick up the disqualified spherical fuel elementswith escaped coated fuel particles falling into the fuel-free zone.Fig. 2 is a structural diagram of the system, which contains atransport and detection platform, a monitoring and protection

Fig. 2. Structural diagram of a spheri

platform, and an operation platform. Transport and detectionplatform is responsible for transferring fuel elements to inspectingposition, generating cone-beam X-ray and collecting projections atdifferent imaging views, which mainly includes X-ray source,transmission line, flat-panel detector and rotary stage. Monitoringand protection platform is a lead shielding room where the trans-port and detection platform is assembled, and a video system usedto monitor the working condition inside the shielding room isequipped. The operation platform is a host work station systemwhere the main-control and imaging software are installed. All thecontrol terminals such as X-ray source, transmission line, flat-paneldetector, PLC (programmable logic controller) controller aremanaged by the host work station through an interactivecommunication means. Also, the host work station is responsiblefor recognizing images and finally sending an evaluation conclusionto the transmission control terminals to isolate the disqualified fuelelements.

Fig. 3 is the inspection flow chart of the developed DR im-aging system. When an inspection starts, a batch of sphericalfuel elements are loaded onto the transmission line and arecarried to the rotation stage one by one automatically. When theinspected spherical fuel element is absorbed by a suction cupfixed on the rotation stage, the X-ray source generates cone-beam X-rays while the fuel element is rotating step by step atthe drive of the rotation stage. Meanwhile, the flat panel de-tector collects DR projections of the spherical fuel element atdifferent imaging views during the rotation and transmits themto the host workstation. A total of 31 frames of DR image arecollected and stored in a buffer memory of the host workstation.Recognition software in the host workstation processes each DRimage in sequence and identifies whether there are escapedcoated fuel particles in the fuel-free zone, once escaped fuelparticles are detected in one image, a ‘Disqualified’ conclusionabout the current fuel element will be drawn and the hostworkstation will inform the transmission control terminals torelease the current fuel element through the disqualified outlet.If all the 31 frames of DR image are recognized and no escapedcoated fuel particles are found, the inspected fuel element willbe considered as a ‘Qualified’ one and exported to the qualifiedoutlet.

cal fuel element testing system.

Fig. 3. A flow chart of escaped coated fuel particles inspection.

M. Yang et al. / Energy 68 (2014) 385e398388

3. Blind-zone-related scanning parameter optimization

DR image is the integration of the material’s attenuation coef-ficient along a cone-beam X-ray, namely it is a 2D image matrixwith overlapping information. As shown in Fig. 4(a), although thereare several escaped fuel particles in the fuel-free zone, when theyare projected onto the detector, their projection will overlap withthe fuel zone’s projection. We simulated a projection of a sphericalshell with 60-mm outer diameter and 50-mm inner diameter, torepresent the fuel-free zone of a spherical fuel element. There is asphere with 0.92-mmdiameter at the top of the inner surface of theshell, namely Particle 1 illustrated in Fig. 4(a). Fig. 4(b) is thesimulated DR image. We can find that most part of Particle 1’sprojection falls into the projection area of the inner sphere, whicheasily brings out an error judgment that the particle lies in the fuelzone, but actually it is an escaped particle. Here we define the re-gion where Particle 1 lies as blind zone. Therefore, our core task isto design the scanning ways and optimize its parameters todecrease the blind zone, and finally to ensure all the escaped coatedparticles can be detected accurately through DR images.

Fig. 5(a) is the 3D geometrical configuration of cone-beam DRimaging. When cone-beam X-rays intersect a spherical shell with60 mm outer diameter and 50 mm inner diameter along one di-rection (Y-direction), there will be two blind zones in the fuel-freezone. Projections of escaped coated fuel particles in these zoneswillbe covered by the projection of the fuel zone. If the spherical shellrotates around a fixed axis (Z-axis), the volume of the blind zoneswill decrease. From the view of engineering application, consid-ering the inspection efficiency and ease of implementation, weadopted a step-by-step circular scanning trajectory to make theinspection rapid. After the spherical shell completes a 360� rota-tion, the blind zone will be reduced to a prismatic spherical shellwith two pointed poles, as shown in Fig. 5(b). On further analysis,we can find that in the two poles and on the equatorial plane, thethickness of the blind zone reaches the largest value. The thicknessof the blind zone is determined by the rotation step angle and thedistance from the X-ray focus to the rotation center. Here we definethem as q and L, respectively.

Fig. 6 is a longitudinal section-diagram of Fig. 5(b) on the XOZ-plane, where the distance from the center of the spherical shell tothe cone roof of the polar blind zone is defined as D0. The diametersof the fuel-free zone and the fuel zone are D0 (60 mm) and D1(50 mm), respectively, and the half cone angle of the X-ray beam isdefined as a. From Fig. 6, we can find that the largest particle whichcan be contained by the polar blind zone entirely is the inscribedsphere of the polar blind zone. We use the radius of the inscribedsphere Rpole toweigh the volume of the polar blind zone. The radiusof one coated fuel particle is defined as rparticle, and we use x¼ Rpole/

rparticle to measure the containing capacity of the polar blind zone.Theoretically, as long as x < 1, the escaped coated fuel particle willexceed the polar blind zone and may be detected. x ¼ 0 indicatesthat the polar blind zones will disappear and the X-rays are parallelbeams. From Fig. 6 we can get

L ¼ D1

2 sin a; (1)

D0 ¼ D1

2þ Rpole þ

Rpolecos a

; (2)

cos a ¼ D1

2D0 : (3)

Combining Eqs. (2) and (3), we get

cos a ¼ D1 � 2RpoleD1 þ 2Rpole

: (4)

When D1 ¼ 50 mm, rparticle ¼ 0.46 mm, we finally get

L ¼ D1=2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1�

�D1�2rparticle,xD1þ2rparticle,x

�2r ¼ 25ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1�

�50�0:92x50þ0:92x

�2r : (5)

Fig. 7 is a cross-sectional diagram on the equatorial plane (XOY-plane) of the cone-beam scanning at two adjacent rotating posi-tions, where we can find that the largest particle which can becontained by the equatorial blind zone entirely is also its inscribedsphere. We use the radius of the inscribed sphere Requator to weighthe volume of the equatorial blind zone, and m ¼ Requator/rparticle isused for measurement of the containing capacity of the equatorialblind zone. Theoretically, as long as m < 1, the escaped coated fuelparticle will exceed the equatorial blind zone and may be detected.m ¼ 0 indicates that the equatorial blind zones will disappear andthe X-rays are parallel beams. From Fig. 7 we can get

b ¼ q

2; (6)

OE ¼ D1

2þ Requator þ Requator

cos b; (7)

cos b ¼ D1

2OE: (8)

Combining Eqs. (6)e(8), we get

Fig. 4. Illustration of an escaped particle’s projection. (a) shows the geometrical sketch of cone-beam DR imaging. (b) shows a simulated DR image of an escaped particle, fromwhich we can find that most part of the escaped particle’s projection falls into the projection of the fuel zone.

M. Yang et al. / Energy 68 (2014) 385e398 389

cosq

2¼ D1 � 2Requator

D1 þ 2Requator: (9)

� �

Hence we finally get

q ¼ 2 cos�1

D1 �2rparticle,mD1 þ2rparticle,m

!¼ 2 cos�1

�50�0:92,m50þ0:92,m

�: (10)

According to Eqs. (5) and (10), we find that L monotonicallydecreases with the increase of x, and q monotonically increaseswith the increase of m. Smaller values of x and m indicate that theescaped coated fuel particles are more likely to be detected. Forexample, while x ¼ 0.01, the corresponding value of L is921.6818 mm. In this case, if a coated fuel particle lies in the polarblind zone, about 99.98% of its volume will exceed the polar blindzone. Meanwhile, when m ¼ 0.04, the corresponding value of q is6.2161�. In this case, if a coated fuel particle lies in the equatorialblind zone, about 99.73% of its volume will exceed the equatorialblind zone. Thus we have reason to believe that, under the condi-tion that L ¼ 921.6818 mm and q ¼ 6.2161�, escaped coated fuelparticles can be detected by the automatic recognition software. Forour developed inspection system, we set L ¼ 700 mm and q ¼ 6�.The corresponding values of x and m are 0.0173 and 0.0373,respectively. In this case, more than 99% of the escaped coated fuelparticle’s volume will exceed the equatorial and polar blind zonesand be projected onto the fuel-free zone on the imaging plane ofthe flat-panel detector. Then, through image-processing and

recognition algorithms, the escaped coated fuel particles are easilydetected on the DR images.

4. Dynamic calibration of the fuel-free zone

The tracking of escaped coated fuel particles is based on theirprojection information in the fuel-free zone. Therefore, beforeescaped coated fuel particles are recognized, the fuel-free zone onthe original projection image must be determined accurately.Theoretically, all fuel elements should have the same sphericalshape, but, due to the random instability of the manufacturingprocess, shape uniformities always exist in the finished fuel ele-ments. Also, the transport and rotation motion can’t ensure thateach spherical fuel element to be inspected is projected onto thesame position on the detector. Thus, it is not accurate to use a maskwith the same shape and fixed location on the imaging plane tocover the fuel-free zone during the course of recognition. Asmentioned above, a fuel-free zone is a region defined by twoconcentric circles, one circle is the outer border of a spherical fuelelement’ projection, the other is a concentric circle whose diameteris 10 mm shorter than the outer one. Here we propose a dynamiccalibration method to trace the fuel-free zone.

Step1. Tracing the outer contour points of a spherical fuelelement’ projection with high accuracy: For a DR image of aspherical fuel element as shown in Fig. 8(a), its gray-level distri-bution is in three classes, namely, the background data with the

Fig. 5. Illumination of the blind zones under a step-by-step circular scanning trajec-tory. (a) shows the blind zones when cone-beam X-rays intersect a spherical shellalong one direction (Y-axis). (b) shows the blind zones when a cone-beam X-ray ro-tates 360� around the spherical shell’s center (Z-axis) step by step.

Fig. 6. A longitudinal section-diagram of the polar blind zone.

M. Yang et al. / Energy 68 (2014) 385e398390

highest gray values, the graphite matrix’s projection data withmedian gray values, and the coated fuel particles’ projection datawith the lowest gray values. Fig. 8(b) is the histogram of Fig. 8(a),we can find that its gray values obviously distribute in three classes.First, image segmentation is applied to the original DR image toseparate the spherical fuel element’s projection from the back-ground by image thresholding. We used Ostu histogram-shape-based image thresholding to perform automatic segmentation.The algorithm assumes that the image to be thresholded contains abi-modal histogram (e.g., foreground and background), then cal-culates the optimum threshold separating those two classes so thattheir combined spread (intra-class variance) is minimal [33]. From

Fig. 8(b), we can find that there is an obvious watershed betweenthe background gay-level and the foreground gray-level on theactual DR image. Thus, an optimal threshold value could be ob-tained through Ostu algorithm. In order to verify the fluctuation ofthe threshold values calculated by the segmentation algorithm, wechose 300 DR images of different spherical fuel elements andadopted Ostu segmentation to binarize them. Fig. 8(c) shows thedistribution of the threshold values, which demonstrates thatquality of the actual input images were very stable for accuratesegmentation. After the optimum threshold is determined, theoriginal gray-level image is simplified to a binary image. Then, edgedetection, image thinning, and image tracing are applied to thebinary image to obtain one closed-circle outer contour of the fuel-free zone as shown in Fig. 8(d). During this step, we use themoment-match based image tracing algorithm to reach a sub-pixel-accuracy edge tracing. First, the pixel-accuracy detection bythe Canny operator is adopted, which has a single-pixel responseand a good performance for improving the signal-to-noise ratio andthe location accuracy. Then, based on the Canny-edge with pixelaccuracy, coordinates of the contour points with sub-pixel accuracyare acquired by solving the equations derived by an assumptionthat the ideal mixed moment is equal to the actual mixed momentof the image [34,35]. The outer contour point of the fuel-free zone isdefined as (xi,zi).

Step2. Least-square circle fitting upon the outer contour of aspherical fuel element’ projection: Based on the coordinates ofthe outer contour points, a least-square fitting algorithm is used forcalculating the center of the outer contour. The least-square circle-fitting problem has been stated as: Given a set of points on theplane-XOZ: (xi,zi), i ¼ 1,2,3,.n, find the circle [36,37]:

ðx� x0Þ2 þ ðz� z0Þ2 ¼ r2; (11)

where (x0,z0) is the center of the fitted circle, and r is the radius ofthe fitted circle. In the sense of whole least-square error, the un-derlying concept of least-square circle-fitting is to minimize the

M. Yang et al. / Energy 68 (2014) 385e398 391

mean square distance from the given points to this circle, i.e. tominimize the function

Eðx0; z0; rÞ ¼Xni¼1

hðxi � x0Þ2 þ ðzi � z0Þ2 � r2

i2: (12)

Let k ¼ x20 þ z20 � r2, equation (12) can be written as

E0ðx0; z0; kÞ ¼Xni¼1

�x2i � 2xix0 þ z2i � 2ziz0 þ k

�2: (13)

Mðx; zÞ3Bðx; zÞ and Mðx; zÞ ¼8<: 1

D1,M2U

�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðx� x0Þ2 þ ðz� z0Þ2

q�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxi � x0Þ2 þ ðzi � z0Þ2

q0 else

: (15)

The values of x0, z0 and r can be determined by differentiatingE0(x0,z0,k) in relation to parameters of x0, z0 and k, and forcing thepartial derivatives to be zero. Thus, if E0(x0,z0,k) is minimum, thenvE0/vx0 ¼ 0, vE0/vz0 ¼ 0 and vE0/vk ¼ 0, namely(

vE0

vx0¼ 2

Xni¼1

�x2i � 2xix0 þ z2i � 2ziz0 þ k

�ð�2xiÞ ¼ 0

vE0

vz0¼ 2

Xni¼1

�x2i � 2xix0 þ z2i � 2ziz0 þ k

�ð�2ziÞ ¼ 0

vE0

vk¼ 2

Xni¼1

�x2i � 2xix0 þ z2i � 2ziz0 þ k

�¼ 0

: (14)

By solving the equation set, we finally obtain the center point(x0,z0) of the fitted circle.

Step 3. Determining the inner circle of the fuel-free zone: Thebinary image acquired in Step 1 after image thresholding is definedas B(x, z), where the background pixel value is 0 and the object(foreground) pixel value is 1. When a spherical fuel element is

Fig. 7. A cross-sectional diagram of the equatorial blind zone.

projected onto the detector, the size of its projection will bemagnified M times. Here,M is the GMR (geometrical magnificationratio), which can be calibrated in advance [38,39]. Thus, thediameter of the fuel zone on the DR image, namely diameter of theinner circle of the fuel-free zone will be D1 �M/U pixels, where U isthe physical size of the detecting unit of the detector, and, after thedetector model is determined, U will be a constant. With the fittedcircle center (x0,z0) and the length of D1 � M/U, the inner circle ofthe fuel-free zone can be drawn. Therefore, the fuel-free zone maskM(x, z) can be defined as

From Eq. (15), we can find that M(x,z) is a sub-region of thewhole binary image B(x,z). Thus, for pixels located in M(x,z) region,namely the pixels located between the outer contour (xi,zi) and theinner circle (x � x0)2 þ (z � z0)2 ¼ (D1$M/2U)2, their values are 1,otherwise, their values are 0. After fuel-free zone mask M(x,z) isdetermined, performing an AND operation between the fuel-freezone mask M(x,z) and the original DR image, the data used forrecognition in the fuel-free zone are extracted as shown in Fig. 8(e),where two escaped coated fuel particles exist.

According to the proposed calibration method, the outer borderof the fuel-free zone is obtained by tracing the outer envelopingcontour of each original DR projection, so a minor change in shapeof an inspected fuel element will be reflected by the traced outercontour. Furthermore, based on the outer contour points with sub-pixel coordinates accuracy, least-square fitting, which is recognizeda powerful algorithm to realize polynomial curve approximationwith high accuracy, is used to determine the inner border of thefuel-free zone. Consequently, when the position of a fuel element’sprojection changes, the inner border of its fuel-free zone will shiftcorrespondingly. Thus, dynamic calibration of the fuel-free zone isreached.

5. Recognition method

5.1. Image denoising

Because the developed testing system is used for batch in-spection of the finished spherical fuel elements, the testing effi-ciency must be taken into consideration. The scanning mode weadopted is that the rotary stage rotates with a fast speed and thedetector collects images with a high frame frequency. The rotationstage makes a round within 30 s, and the imaging frame frequencyis 7.5e15 frames per second. In addition, the small detecting unitsize, the high frame frequency of the detector, and the low X-rayexposure result in DR images with high-level noise, which becomesthe inherent limitation of the original DR images. Thus, in the pre-processing step of feature recognition, reduction of image noise isessential to improve SNR (signal-to-noise ratio) performance of theDR images and eventually to enhance the recognition accuracy. Weselected an effective and easily implemented denoising method,which is called an adaptive weighted median filter, as proposed byMustafa Karaman [32]. This method originates from the well-known median filter, and the filtering kernel is obtained througha local statistics-based region-growing technique for adjustment ofthe shape and size of the local filtering kernel. Compared with the

Fig. 8. Illustration of the fuel-free zone calibration. (a) is one original DR projection image of a spherical fuel element with escaped coated fuel particles. (b) is histogram of theoriginal DR image. (c) is the distribution of threshold values calculated by Ostu algorithm for 300 DR images. (d) is a diagram of the fuel-free zone mask. (e) is the fuel-free zoneimage obtained by performing AND operation between the fuel-free zone mask M(x,z) and the original DR image.

M. Yang et al. / Energy 68 (2014) 385e398392

traditional median filter, the adaptive weighted median filtermakes it possible to suppress noise while edges and other impor-tant features are preserved. Meanwhile, in order to realize real-time denoising, the CUDA (compute unified device architecture)framework is adopted, which accelerates the algorithm operationson a GPU (graphics processing unit). The basic idea of GPU accel-eration is tomake full use of themultiprocessor structure and SIMD(single-instruction multiple-data) characteristics of the GPU [40e

42]. The general flow chart of GPU-accelerated denoising isshown in Fig. 9(a). In order to pick up pixel values of the noisyimage quickly, 2D-GPU-texture-array is applied. Meanwhile, thenumber of threads allocated on a GPU is determined by the size ofthe original image. Each thread is responsible for denoising oper-ation in one pixel and all the threads are running in parallel througha kernel function. Denoising filter process is implemented by a GPUkernel function whose operation flow chart is shown in Fig. 9(b).

Import the noisy image into thehost memory

Allocate a cudaArray on GPU memory, and copythe data in host memory to the cudaArray

Define a texture on GPU, and bound the data oncudaArray to the texture memory

Allocate the size of grid and block inGPU for parallel computing

Call the kernel function on GPU to run adaptiveweighted median filtering

Copy the GPU de-nosing result tothe host memory.

(a)

Allocate shared memory on GPU

Extract the data in filter template by threadindex, and save them in the shared memory

Sort pixel gray value in filter template

Assign the median value to the currentpixel in the denoised image

Determine the template sizeof median filter.

Calculate GPU thread indexes according tothe noisy image size

(b)

Fig. 9. Flow charts of GPU-accelerated denoising and kernel function. (a) is a general flow chart of GPU-accelerated denoising. (b) is flow chart of a kernel function running on a GPU.

M. Yang et al. / Energy 68 (2014) 385e398 393

Firstly, a filtering template with an odd number of pixels is deter-mined, and a shared memory on GPU is allocated to store theoriginal data covered by the filtering template. Then, in line withsize of the original image, GPU thread indexes are created toperform adaptive weighted median filtering in parallel. In eachthread, original data in the filtering template are sorted and themedian value is assigned to the current pixel in the denoised image.After calculations in all threads are completed, the final denoisingresults are duplicated to the host memory.

5.2. Recognition method

As shown in Fig. 8(e), two escaped particles exist in the fuel-free zone, and their gray-levels are lower than the background(the graphite matrix) gray-level. We select the ROI (regions of

Fig. 10. Flow chart of the escaped coated fuel particle r

interest) image including the escaped particles and draw itssurface figure as shown in Fig. 10, from which we can find thatthe trough regions happen to be where the escaped particles lie.Thus, the core task for the recognition of escaped particles is toidentify the trough regions quickly and accurately. Fig. 10 is therecognition flow chart we adopted. First, morphologic closing isperformed on the denoised fuel-free zone image with the aim offlattening the trough regions. The image closing operation can beexpressed as [43]

g1ðx; zÞ ¼ ðf ðx; zÞ4bðx; zÞÞQbðx; zÞ; (16)

where g1(x, z) is the closed image, f(x, z) is the denoised fuel-freezone image, and b(x, z) is the structuring element. 4 and Q are amorphologic dilation operator and an erosion operator,

ecognition (the ROI image is marked in Fig. 8(a)).

M. Yang et al. / Energy 68 (2014) 385e398394

respectively. Equation (16) means that morphologic closing isrealized by two steps, first, image dilation, and then image erosionfollows for reaching the final result. Dilation and erosion are twoprincipal morphological operations used in imaging processing.Dilation allows objects to expand, thus potentially filling in smallholes and connecting disjoint objects. Erosion shrinks objects byetching away (eroding) their boundaries. These operations can becustomized for an application by the proper selection of thestructuring element, which determines exactly how the objectswill be dilated or eroded. Thus, whether or not a morphologicalclosing operation can be well done depends upon whether asuitable structuring element can be found, which fits well insideregions that are to be removed. As shown in Fig. 10, the troughregions are the regions to be removed, i.e. the ‘holes’ to be filled inthe image. And the ‘holes’ in the fuel-free zone are exactly theprojection of escaped fuel particles. Therefore, the shape and sizeof the designed structuring element should be close to that of afuel particle’s projection as far as possible. Because the projectionof a coated fuel particle is in round shape, a disk-shaped struc-turing element is preferable to select and its size should be closeto the size of the fuel particle’s projection. During the practicalrealization, when inspecting conditions were fixed, especiallyunder a certain geometrical magnification ratio, we made a sta-tistics of the average size of a fuel particle’s projection, throughmeasuring the size of the minimum bounding rectangle of morethan 100 fuel particles’ projections. We found that, as shown inFig. 11, when the size of the disk-shaped structuring element wassmaller than the coated fuel particle’s projection, the recognizedarea will occupy a lesser percentage of the coated fuel particle’sactual projection area. When the size of the disk-shaped struc-turing element increases to 9 � 9, i.e. the closest average size of afuel particle’s projection, the recognized area becomes very closeto the coated fuel particle’s actual projection area. After themorphologic closing operation, the closed image g1(x, z) is almostthe same as the original fuel-free-zone image except for thecoated-fuel-particle region. Thus, when g1(x, z) subtracts theoriginal fuel-free-zone image, the dominant information in theresulting image will be the projections of escaped coated fuel

Fig. 11. Recognition results of escaped coated fuel particles u

particle, except for leftover artifacts with a very low gray-level.Therefore, Ostu thresholding is employed again for extraction ofthe escaped coated fuel particles.

6. Recognition results

Because the recognition is performed on final projections of aspherical fuel element, image quality, especially the level of imagenoise will be a dominant factor determining the recognition accu-racy. To verify the stability and accuracy of the proposed recogni-tion method, we first simulated DR images of a spherical shell with60-mm outer diameter and 50-mm inner diameter, and placed sixspheres with 0.92-mm diameter in the fuel-free zone. The densityof the shell equals to graphite matrix’s density and the density ofsmall spheres equals to uranium kernel’s density. The gray-level ofthe simulated DR images was normalized to 0e255, and Gaussiannoise with different intensities was added to the simulated DRimages. Then, dynamic calibration of fuel-free zone, imagedenoising and object recognition were applied to the simulatednoisy images. Table 1 is the recognition results including originalnoisy image, profiles of a row data in the original noisy image, therecognition results and the number of identifiable particles. Fromthe results, we can find that even the standard deviation ofGaussian noise (s) reached 20, the six presetting simulated fuelparticles were still able to be recognized.

For a DR imaging system, image noise is mainly from dark cur-rent of the detector, scatter, response nonuniformity of detectingunits, and other random noise. To decrease image noise to a lowlevel as possible, we adopted offset correction and gain correctionto reduce dark noise and response nonuniformity noise [44,45].Furthermore, a collimator with a taper hole was fixed in the outletof cone-beam X-rays to restrain the scattered X-ray photons, whichwas proved to be an effective method of enhancing contrast of DRimage [46]. For other random noise control, increasing integrationtime and X-ray tube current were simple and effective approaches.We also used frame averaging method, which was equivalent toincreasing integration time, to reduce the random noise. Fig. 12 is aDR image of a spherical fuel element which was denoised by these

nder disk-shaped structuring elements of different sizes.

Table 1Simulated noisy DR images and recognition results.

s ¼ 15 s ¼ 20

Original noisy projection

Profile

Recognition results

Number of identifiable particles 6 6

M. Yang et al. / Energy 68 (2014) 385e398 395

methods (including adaptive weighted median filter). The standarddeviation of the background from an actual DR image basicallyreflects the image noise level. Thus, we selected four regions ofinterest (ROI) in the background of an actual DR image as labeled bydashed boxes in Fig. 12. Then, standard deviation of each ROI wascalculated and the results were: Std(ROI-1) ¼ 2.3722, Std(ROI-2) ¼ 2.3443, Std(ROI-3) ¼ 2.3676, Std(ROI-4) ¼ 2.3827, and themean value of the four standard deviations was 2.3667. Fig. 13 is aprofile of a row data in Fig. 12, which demonstrates that gray-leveldifference between background of the fuel-free zone and

projections of coated fuel particles was very distinct. Comparedwith the above simulated noisy DR image, the actual DR image hasfar lower noise level and higher image contrast. Thus, we have thereason to believe that when the proposed recognition method isapplied to actual DR images, its accuracy can satisfy the re-quirements of practical inspection. In other words, the proposedrecognition method has a high ability of anti-noise.

As analyzed before, the dominant scanning parameters whichdecide the size of the blind zone are the distance from the X-raysource to the tested object (L) and the rotation step angle (q). For

Fig. 12. An actual DR image of a spherical fuel element. The dashed boxes in thebackground are the regions of interest, being used to calculate noise level of the actualDR image. The dashed line indicates the position of the profile shown in Fig. 13.

M. Yang et al. / Energy 68 (2014) 385e398396

the DR imaging system for automatically testing spherical fuel el-ements, we set the two parameters at 700 mm and 6�, respectively,to ensure that most part of the escaped coated fuel particle exceedsthe blind zone and is projected onto the fuel-free zone on the im-aging plane. Also, for the geometrical symmetric characteristic ofthe cone-beam X-ray imaging, the spherical fuel element just ro-tates 180� with a 6�-step angle. When the inspection begins, abatch of spherical fuel elements is transported to the rotation stageone by one automatically, and then the current element to beinspected is absorbed by a suction cup and rotates step-by-step atthe drive of the rotation stage. Meanwhile, the flat-panel detectorcaptures the projection images at each imaging view, and a total of31 frames of DR image are collected. The detection period for eachspherical fuel element is less than 30 s. In the practical realization, a

100 200 300 400 500 600

20

40

60

80

100

120

140

160

180

200

Pixel Number

Gra

y Le

vel

Fig. 13. A profile of the DR image in Fig. 12.

queue container is applied in the memory of the host workstationin advance to store the 31 frames of DR image. According to the datastructure of a queue container, the captured projection image isinserted into one end of the container and extracted from the otherend to be processed and recognized, namely, first-in and first-out.After all of the 31 frames are recognized, the conclusion of evalu-ation is sent to the transport control terminals, which finally exportthe tested spherical fuel element to a qualified outlet or a dis-qualified outlet. In the experiments, we designedly manufacturedseveral disqualified spherical fuel elements with simulated TRISO-coated fuel particles and inspected them by the developed DRimaging system. The main scanning parameters were:

Size of X-ray focus: 8e20 mmSize of imaging area: 130 mm � 130 mmPixel size: 127 mmSDD (source to detector distance): 820 mmX-ray tube voltage: 90 kVX-ray tube current: 860 mAFrame frequency: 7.5 fpsAveraging frames: 5

Fig. 14 shows the recognition results, with the fuel-free zoneborderline and the escaped coated fuel particles labeled in differentcolors (inweb version). As shown in Fig. 14, on some DR images, theescaped coated fuel particles seem in the fuel zone scope, which isdue to the property of the geometrical configuration of the cone-beam DR imaging system. Therefore, the final conclusion of eval-uation must be upon all the DR projections collected during thewhole testing cycle.

7. Conclusions

In order to realize fast and accurate detection of escaped coatedfuel particles in the fuel-free zone of a spherical fuel element, X-ray DR imaging under a step-by-step circular scanning trajectorywas adopted. The scanning parameters dominating the volume ofthe blind zones, namely, the rotation step angle (q) and the dis-tance from the X-ray source to the tested object (L) were opti-mized to ensure the missing detection of escaped coated fuelparticles is as low as possible. We find that the size of the polarblind zone decreases monotonically with the increase of L,whereas the size of the equatorial blind zone decreases mono-tonically while q drops. For the DR imaging system design, we setthresholding values for L and q, such as L ¼ 700 mm and q ¼ 6�,this can ensure that most part of an escaped coated fuel particleexceeds the blind zone and is projected onto the fuel-free zone onthe imaging plane. Next, because of the shape uniformities and theinstabilities of transport and rotation motions, the fuel-free zone isnot always projected onto the same region on the imaging plane ofthe detector, and is not always in the same shape and fixed size. Adynamic calibration method for accurately tracking the projectionof the fuel-free zone is proposed, instead of using a fuel-free-zonemask of a fixed size and position. After the projection data in thefuel-free zone are extracted, image and graphic processingmethods are combined for automatic recognition of the escapedcoated fuel particles. The recognition task here is to check whetherthere are escaped coated fuel particles in the fuel-free zone. Thedifference between the escaped fuel particle’s actual projectionarea and the labeled area by recognition was not discussedquantitatively in this paper. However, during the design of therecognition algorithm, the shape and size of the structuringelement and the image noise level are important factors fordetermining the recognition precision.

Fig. 14. Recognition results for a disqualified spherical fuel element. (a) is the original DR image and the recognition result at a 48� rotation position. (b) is the original DR image andthe recognition result at a 96� rotation position. (c) is the original DR image and the recognition result at a 144� rotation position.

M. Yang et al. / Energy 68 (2014) 385e398 397

Acknowledgments

This work was supported in part by the National NaturalScience Foundation of China (NSFC) under Grants No.11275019,No.21106158, and No.61077011, in part by the National S&TMajor Project under Grant No. ZX06901, in part by the National

Key Technology R&D Program of China under GrantNo.2011BAI02B02, in part by the National State Key Laboratoryof Multiphase Complex Systems under Grant No.MPCS-2011-D-03, and in part by the National Key Scientific ApparatusDevelopment of Special Item of China under GrantNo.2013YQ240803.

M. Yang et al. / Energy 68 (2014) 385e398398

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