An image processing system for track-etch detectors

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Radiation Measurements,Vol. 26, No. 2, pp. 187-191, 1996 Copyright © 1996ElsevierScience Printed in Great Britain.All rights reserved 1350-4487(95)00306-1 !350-4487/96 $15.00+ 0.00

Ltd

Pergamon

AN IMAGE PROCESSING SYSTEM FOR TRACK-ETCH DETECTORS P. KOKKAS, P. KOKKINIDIS,* P. PAVLOPOULOS and S. VLACHOS Institute for Physics, University of Basel, Klingelbergstr. 82, CH-4056 Basel, Switzerland (Received 14 July 1995; in final form 9 November 1995)

Abstract--An automated recognition system has been developed which identifies, counts and classifies nuclear tracks on solid state nuclear track detectors (SSNTD). Special care is taken for overlapping tracks, allowing analysis of films with high track densities up to 60,000 tracks/cm 2. The performance of the proposed method, with a counting error of 3% for a track density of 30,000 tracks/era 2, is superior to conventional track counting techniques when evaluating either CR-39 or Lexan SSNTDs. The recognition system evaluates each image in less than 2 s with a PC-486 at 66 MHz, allowing its integration in an on-line automated track detection set-up.

• Good classification power for both tracks emerging from passing o-particles and fission fragments. • Relatively high execution speed.

1. INTRODUCTION Solid state nuclear track detectors (SSNTD) provide a convenient method for registering ionizing charged particles. Each such particle going through an SSNTD foil leaves a permanent trace. The properties of materials to be used as SSNTDs, their registration efficiencies for various particles as well as the required experimental tools and procedures are well established by now (for a review see, for example, Durrani and Bull, 1987 or Fleisher et al., 1975). Tracks left from passing particles are only made visible upon etching in a strong base solution. These tracks are then examined using standard optical transmission microscopy. In most applications, tracks are counted and classified manually in order to obtain a high degree of counting precision. Here we describe an automatic track counting and classification procedure. The system developed was used to evaluate SSNTD foils exposed to fission fragments originating from neutron captures in natural uranium foils. Two kinds of foils were studied: CR-39 (manufactured by Pershore Mouldings Limited) and Lexan (provided by General Electric Plastics). Their main difference was that where the latter (Lexan) registers only fission fragments (being insensitive to the or-radiation), the former (CR-39) registers both kinds of particles. F o r a large number of microscope optical fields our automated track evaluation system has the following properties: • High accuracy in track counting. • High signal acceptance and high background rejection.

2. M E T H O D The overall task is to count, identify and classify the recorded tracks, given their digital image obtained through a microscope. On an obtained image a track is defined as an object with specific properties, such as gray levels, size, shape etc., which satisfies certain constraints in shape, size, intensity, position, etc. The process of defining such objects is known as feature detection or segmentation (Rosenfeld, 1988). There are three major classes of segmentation methods: 1. Threshold techniques. 2. Computation of derivatives. 3. Template matching. Algorithms using threshold techniques are the easiest to implement, but they have serious performance drawbacks. The main is their limited ability to localize precisely the boundaries of the regions of interest, in particular in cases of noisy images. Template techniques on the other hand, especially in their simplest forms, depend on the size of the objects to be extracted. Improved methods that overcome this disadvantage require an excessive computing effort. In the application considered here, the most adequate feature is the step edge, defined as an abrupt change of the gray level. An abrupt gray-level change corresponds to a maximum in the first derivative of

*Visitor from University of Athens, Greece. 187

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the image, which can in general be precisely located by detecting the zero-crossings of the second derivative of the image (for details see Niemann, 1989). In real life images however, noise may cause ambiguities in the selection of the proper zero-crossing, hence a smoothing preprocessing of the image is required. The smoothing operator to be used should be limited in space as otherwise the image will lose much of its actual content. In addition it should be band-limited to restrict the edge detection in a certain frequency interval. The best compromise that satisfies both criteria is the Gaussian function (see Gonzalez and Woods, 1993): l

g(x,y)=~e

x2+y2 2,2 .

o,~

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The input image l(x,y) is convoluted with a Gaussian function and then the second derivative is calculated. The output image O(x,y) is given by:

O(x, y) = -V2[l(x, y) ® g(x, y)

x I(x', y')g(x' -- x,y' --y) dx' dy'

=[-V2g(x,y)] ® I(x,y).

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Fig, 1. The band-pass filter used in our track selection algorithm for the one-dimensional case. The two lines correspond to the filter function G(x)= ( 2 ~ ) - 1 / 2 0 " - 5 ( X 2 -- o'2)e -(x~/2a2) for two different values of a. Function G(x) is the equivalent of (3) for the one-dimensional case. The solid line is a graph of G(x) as a function of the free parameter x, with tr = 1. The dashed line corresponds to the same function with tr = 2.

(2)

Therefore the whole operation can be performed by the convolution of the input image with the function: l

,~ o

X2 + y2

V2g(x,y) = .___~_~__5(x2 + y 2 _ 2tr2)e-~T-,

(3)

x/2na

The above function represents a band-pass filter whose center frequency depends on a. A plot of that function for the one-dimensional case is shown in Fig. 1 for two difference a values. High tr values reduce the locality of the filter and thus the noise by smoothing the image, whereas smaller values of tr give fine details in gray-level changes, with increased noise sensitivity. An example of the above transformation (2), for an one-dimensional case, is shown in Figs 2 and 3. Figure 2 shows the function to be transformed, and Fig. 3 shows the transformed function. The input function consists of three Gaussian distributions in positions 20, 50 and 80, all with a variance equal to 3. These Gaussian distributions are superimposed on a sigmoidal function ( y = 3/(1 + e -Cx-5°)/2) + 1) which passes from 1 to 4 when x goes over 50. It can be clearly seen that the resulting function is independent of the background level in the original image, a n d so peaks can be detected at the same time without having to adjust any local threshold for each individual case. In addition all identified peaks have the expected widths. The output of this transformation can be used to find the zero-crossing points, which indicate step edges. However some background variations in the original image are still detectable in the filtered image. Hence we introduce a level for the pixel value of the transformed image, which is higher than zero and set

as a percentage of the output range. Each pixel is set to 0 if its value is below that level and is set to 1 otherwise. This binary image is further analyzed to locate objects (i.e. isolated regions with all pixels valued 1). When an object is found it is classified according to its size and any point-like object is rejected as noise. Each object traced is fitted with an ellipse. If the quality of the fit is not satisfactory (i.e. Z2 of the fit

× 5 4

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Fig. 2. A one-dimensional function l(x) plotted as a function of its argument x, used as an example to be transformed by the filter G(x) (the one-dimensional equivalent of the two-dimensional filter given in (3)).

IMAGE PROCESSING SYSTEM FOR TRACK-ETCH DETECTORS

For each image the system provides the following values:

0 0.1

• Total number of objects found; • Number of rejected objects (i.e. objects not inside the size limits set); • Number of single and multiple objects found in the frame; • Size distribution of all objects (histogram); • Size distribution of selected objects (histogram); • Length distribution of all objects (histogram); • Length distribution of selected objects (histogram).

0.08

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Fig. 3. The one-dimensional transformation of the function shown in Fig. 2 obtained through (2). The figure shows the transformed function O(x) as a function of its argument x.

greater than 2) the object is considered as two overlapping tracks. The probability of having three of more overlapping tracks is too small to consider those cases independently.

3. IMPLEMENTATION The above algorithm was developed in C language on a PC computer with an INTEL 80486 CPU running at 66 MHz. The resulting program handles 8-bit gray-scale images with 512 x 512 pixels or less. The system processes automatically a full set of images without external intervention. The results of the processing of each image can be saved in a text file for review and analysis. The user has the freedom to change the following parameters of the system: • Filter level: as already mentioned above, there is a trade-off between noise sensitivity and localization precision. By adjusting the filter level, the user can select the optimum level for each kind of images evaluated. • Shape parameter: this parameter defines a cut-off value on the X2 distribution, in order to classify objects belonging to class of shapes, i.e. straight lines, circles, etc. • Size o f objects: as the processing of exposed foils, and therefore the size of tracks of interest, may vary from an application to another, the user determines the range of sizes of the objects to be classified and counted in any single class. The proper values for the size parameters can be established by inspecting the histograms provided by the system.

The average process time per frame is 1.6 s for a 256 x 256 image, and 3.5 s for a 512 x 512 one. The program does not only measure the track density per optical field in a global way, but tags each individual region of the image considered as a track, which can be further analyzed. 4. TRACK COUNTING PERFORMANCE The above system was developed for the analysis of Lexan and CR-39 foils in a subcritical reactor exposed to a high energy proton beam (Andriamonje et al., 1995). However, it was first evaluated and its performance was measured using foils exposed to radiation from radioactive sources. Lexan foils were irradiated by a :5~Cf source and CR-39 foils by an 241Am source. Note that Lexan registers only fission fragments and not or-particles. After proper chemical etching each developed foil was observed via an optical microscope. This had an optical field of 315 x 315/~m 2. The images obtained were both analyzed manually and presented to the system for automatic evaluation. The manual track counting provided a highly accurate estimation of the number of tracks in each optical field, used to control the results of the automated counting. The samples used for this evaluation had a track density of about 30,000 tracks/cm 2. The main variables that describe the performance of our counting system are: • Efficiency o f track finding: Our system has an efficiency of 98%. This means that 98% of all the manually identified tracks were labeled as such also by the system. • Background acceptance: i.e. identification of nontrack areas as tracks. The above system counts 3% more tracks than those identified manually, which belong to background noise.

It should be pointed out that both parameters are equally important for a reliable system. To study further the stability of the track counting method proposed, images with artificial white noise added to them were also presented to the system. For noise levels of up to 15% (i.e. where 15% of the image's pixels were flipped) the system's performance stayed virtually unaltered.

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The error on the total number of tracks is 1% for track densities in the range of 30,000 tracks/cm 2. The corresponding error including the classification of ~t-particles and fission fragments varies between 2 and 3%. When counting only s-particles (fission fragments) the error is 3% (2%). These errors are expected to be considerably lower for track densities of about 5000 tracks/cm 2. To study further the performance of our track recognition system, we used Lexan foils exposed for a determined time period to a calibrated radioactive source. In this way we evaluated the system in realistic conditions. We used a calibrated 252Cfsource. Lexan foils were exposed to it by direct contact for minimal losses. Exposures of various time durations were executed. All the exposed foils were subsequently etched together for 2 h in a NaOH 6 N solution at a temperature of 55°C. After proper cleaning of the etched foils, the samples were ready for microscope observation. The microscope used for this observation was connected to a CCD camera that obtained images of the optical fields and transferred them to an image frame-grabber of a PC computer. The camera used was a high-resolution (512 x 512 pixels) low-light sensitive (0.3 Lux) 1/3" CCD one, with an incorporated automatic iris facility (which allowed a constant image intensity level, independent of the lighting conditions). The optical field of the images on the computer was measured (via calibrated gratings) to be 315 x 315 # m 2. The track counting program was executed for each image counting the number of tracks and thus evaluating the track density on each Lexan foil. Figure 4 shows the measured track density as a function of the exposure time of the Lexan sample to the radioactive source, On the same plot a line is drawn representing the expected track density as a function of the exposure )

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Fig. 4. Measured track densities on a Lexan foil as a function of the exposure time to a 25~Cfcalibrated source. The straight line drawn represents the expected track density to be observed on the foils, for the given geometry and intensity of the source.

time. The experimental points show an excellent linear behavior up to the highest track densities where the measurements deviate from linearity. Even if this deviation is attributed solely to the performance of the track recognition system, it is still proven that the track counting is precise for track densities up to 60,000 tracks/cm 2. We analysed 400 images of Lexan each with a mean track density of 30,000 tracks/cm 2. Since Lexan registers exclusively fission fragments, we could evaluate the probability that the system identifies fission fragments as s-particles or background noise. We have measured that 97% of the fission fragments were correctly identified, 2% were identified as orparticles and 1% was considered as noise. It should be noted here that even if Lexan is not sensitive to or-radiation our measuring system could misidentify fission fragment tracks as ones from or-particles. This is so because this system is capable of recognizing tracks from both fission fragments and or-particles on any exposed foil. Hence tracks on Lexan foils that are smaller than the preset minimum size for fission fragment tracks are considered as originating from or-particles (if they are not so small as to be considered as background noise). Analyzing an equivalent number of CR-39 foils exposed this time to or-radiation we observed that the system tagged as or-particles 95% of the actual ones. It has misclassified 3% of them as fission fragments and 2% as background. Finally non-exposed (but developed) foils of both kinds were presented to the system. It classified 97% of the visible tracks as noise, 2% of them as ~t-particles and 1% as fission fragments. 5. C O M P A R I S O N WITH OTHER M E T H O D S Presently many automatic track measuring systems are in use. Most of them tackle the problems of film scanning, microscope focusing and image registration. The track identification is normally done by the operator. In the cases where a track identification program is incorporated in the system, this is usually based on simple thresholding techniques (Chambaudet et al., 1991; Viques et al., 1991; Yisheng et al., 1993). In the binary images obtained after thresholding the number of areas with set pixels is counted. Owing to the threshold technique such algorithms have an intrinsic performance, in general inferior to the one of the system presented here. For example, systems described in Viques et al. (| 991) and Yisheng et al. (1993) count the number of tracks with an error of 8-12% in SSNTDs with track densities of the order of 6000 tracks/era 2. Systems based on commercially available automatic track measuring systems (Chambaudet et aL, 1991) have a measurement error of 4% for the same low track density. These errors increase with higher track densities. The above numbers should be compared with the measurement error of 3% of our system for track densities of the order of 30,000 tracks/era 2.

IMAGE PROCESSING SYSTEM FOR TRACK-ETCH DETECTORS In addition most of the algorithms are sensitive to noise in the images and are appropriate for only restricted classes of tracks (they can, for example, select only fission fragment tracks as the or-particles leave much smaller traces). On the contrary our method is quite general and can be used in a variety of cases with minimal adjustment.

6. CONCLUSIONS In the previous sections a track detection method has been described. It was used to count tracks in SSNTDs. The system performed appropriately in various experimental conditions (white or dark field microscopy, clean or noisy images). Its performance almost reached that of human operators, and was well above that of simple thresholding detection systems. Implemented on a typical PC platform, the algorithm counts the number of tracks in an image with an error of 3%, in less than 2 s. This allows the use of the system as a part of an on-line image capture and track evaluation method. Acknowledgement--This work was supported by the Swiss

National Science Foundation.

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REFERENCES Andriamonje S., Angelopoulos A., Apostolakis A., Attale A., Brillard L., Buono S., Calero J., Carminati F., Casagrande F. and Cennini P. (1995) Experimental determination of the energy generated in nuclear cascades by a high energy beam. Phys. Let. B 348, 697-708. Chambaudet A., Berger F., Klein D., Fellmann D. and Barillon R. (1991) Development of an automatic image scanner for nuclear track analysis. Nucl. Tracks Radiat. Meas. 19, 247-248. Durrani S. A. and Bull R. K, (1987) Solid State Nuclear Track Detection. Pergamon Press, Oxford. Fleisher R. L, Price P. B. and Walker R. M. (1975) Nuclear Tracks in Solids. University of California Press, Berkeley. Gonzalez R. and Woods R. (1993) Digital Image Processing. Addison-Wesley, New York. Niemann H. (1989) Pattern Analysis and Understanding. Springer, Berlin. Rosenfeld A. (1988) Computer vision: basic principles. Proc. IEEE 76, 863-868. Viques S., Espinosa G. and Castafio V. M. (1991) Image processing analysis of chemical tracks. Nucl. Tracks Radiat. Meas. 19, 271-272. Yisheng L., Deyan T., Xiaohai H., Ying Z. and Di A. (1993) Automated image analysis system for etched track counting--a preliminary study. Nucl. Tracks Radiat. Meas. 22, 217-218.