The document describes the development and implementation of an automatic track counting system for CR-39 solid state nuclear track detectors. The system uses a MATLAB software program to automatically count tracks from microscope images of etched CR-39 detectors. CR-39 detectors were exposed to different radiation levels and counted manually and automatically. Results found the automatic counts had up to 30% difference from manual counts, with the new digital microscope providing more accurate automatic counts than the traditional microscope. The system takes less than one minute to count tracks per detector. Future work aims to improve counting of overlapping tracks and develop an automatic thresholding algorithm.

1. DEVELOPMENT AND
IMPLEMENTATION OF AN
AUTOMATIC COUNTING
SYSTEM FOR CR-39 SSTND
Name: Moayyad Mazen
Alssabbagh
Matric No.: MGQ120003
Supervisor: Mr. Tan Li Kuo
Co-supervisor: Prof. Kwan-Hoong Ng and Dr. Vincent Phua
UNIVERSITI
M A L A Y A
MEDICAL PHYSICS RESEARCH PROJECT
MGQG6189

2. INTRODUCTION
• The CR-39 solid state passive nuclear track detector is a
popular method to measure charged particle and neutron
radiation due to its low cost, robustness, track
permanence, and insensitivity to Gamma, X-ray, Beta and
Electromagnetic waves.
• Heavy charged particles passing through these passive
detectors leave a narrow trail of damage; this damage is
visible at modest optical magnification levels.
• Traditional manual counting methods are labor intensive
and highly operator dependent.
11 July 2013

3. 11 July 2013
CR-39
Damage trails or Tracks

4. 11 July 2013
• The main aim of this research is to develop
an affordable automatic CR-39 track
counting system.
• Comparing the obtained results from the
software with the manual ones.
OBJECTIVES

5. BACKGROUND AND LITERATURE
REVIEW
• CR-39 detectors are constructed from a polyallyl diglycol
carbonate (C12H18O7)
• Applications: Radon dosimetry, Personal neutron
dosimeter.
• Advantages: inexpensive, insensitive to EM, simple
processing method, robust, transparent and can be cut
into small pieces.
• Disadvantages: not reusable, counting is time consuming
(manual counting), Commercial digital system (expensive)

6. BACKGROUND AND LITERATURE
REVIEW (Cont.)
• The characteristic of tracks: small light spot and
uniform shape (circular or elliptical)
• The diameter is affected by:
1. etching conditions (time, temp.,
concentration … etc.) and
2. The energy of incident particles
Artefacts
Tracks

7. BACKGROUND AND LITERATURE
REVIEW (Cont.)
• Most track-reading systems use:
1. conventional optical microscopes.
2. high resolution charged couple device (CCD) camera to capture the magnified
picture.
• Commercial software packages used for track counting, such as:
• Image-Pro
• Free software as Image-J
• Monte Carlo simulation
• Others used MATLAB® (Patiris, Blekas, & Ioannides, 2007) (Ahn & Lee, 2005; Puglies, Sciani, Stanojev
Pereira, & Pugliesi, 2007) (Dwaikat, et al., 2010) (Zylstra, et al., 2012).

8. METHODOLOGY
EQUIPMENT USED
• Traditional Microscope
• Full Digital Microscope
• Stage micro-ruler microscope slide
• CR-39 detectors.
• Ra-226 with activity of 122KBq (AECS)
• Chemical solution (NaOH)
• MatLab software v. R2009a
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9. METHODOLOGY (Cont.)
• A set of CR-39 detectors with dimensions of 1.5 cm x 1.3 cm were
exposed to Ra-226 for different time periods (1, 2, 4, 8 and 12 hours).
• 6 M of NaOH solution at a 70 ᵒC over 7 hours used for etching process.
• Two Microscopes were used to capture the images of the detectors:
• The first one was a traditional light microscope with a digital camera
connected to the eye piece. (0.3 MP)
• The second microscope was a full digital one with an LCD monitor with
an area of 3.5” which acts as a 10x eyepiece. (2 MP).
• The same magnification (40x) was used for both microscopes.
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10. METHODOLOGY (Cont.)
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• Manual and Automatic counting were performed for
both images from old and new microscope.
• A comparison between manual and automated
counting is performed.

11. RESULTS
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Manual counting.: Traditional microscope and the Digital Microscope
20 scenes have been captured from each detector. Manual counting has been done
and the average was calculated.
(Traditional: 0.7 mm x 0.55 mm) (Digital: 1 mm x 1.3 mm)

12. RESULTS (Cont.)
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Automatic counting.: Traditional microscope and the Digital Microscope
Automatic counting has been done for the 20 scenes of each detector and the
average was calculated.

13. RESULTS (Cont.)
Manual counting.: Traditional microscope and the Digital Microscope
Table 1: The difference of reading for both manual and automatic counting (first & second
microscopes)
First microscope Second microscope
Exp. time
(hr)
Radon Con.
kBq.h/m3
Manual
counting
Auto.
counting
Difference
in reading
Manual
counting
Auto.
counting
Difference
in reading
1 170 973 850 14.9% 516 550 6.6%
2 340 1671 1565 6.3% 1094 1124 2.7%
4 680 3052 2750 9.9% 2976 2927 1.6%
8 1360 5434 3789 30.3% 5857 5857 0.0%
13 2210 9157 6144 32.9% 10713 10602 1.0%

14. RESULTS (Cont.)
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Calibration curves for manual counting:
Traditional microscope and the Digital Microscope
973
1671
3052
5434
9157y = 4.1467x
R² = 0.9946
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
9000.00
10000.00
0 500 1000 1500 2000 2500
Density(Tr/cm2)
Radon Exp. (KBq.hr/m3)
Calibration curve for manual counting
(Traditional Microscope)
516 1094
2976
5857
10713y = 4.6491x
R² = 0.9892
0.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
0 500 1000 1500 2000 2500
Density(Tr/cm2)
Radon Cons. (KBq.hr/cm3)
Calibration curve of manual counting
(New Microscope)

15. RESULTS (Cont.)
11 July 2013
Calibration curves for Automatic counting:
Traditional microscope and the Digital Microscope
868
1434
2421
3789
6144y = 2.8629x
R² = 0.9648
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
0 500 1000 1500 2000 2500
Density(Tr/cm2)
Radon Exp. (KBq.hr/cm3)
Calibration curve for automatic counting
(Traditional Microscope)
550 1124
2927
5857
10602y = 4.6133x
R² = 0.9907
0.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
0 500 1000 1500 2000 2500
Density(Tr/cm2)
Radon Cons. (KBq.hr/cm3)
Calibration curve of Automatic Counting
(New Microscope)

16. DISCUSSION
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Elliptical
tracks
Circle
tracks
Bubbles
Defects
De-noising the image
means to remove the
scratches, bubbles and
small dots that are
caused by the camera
or the detector itself.
To achieve a clear and
acceptable image, the
program depends on
three Steps:
1. Binary threshold
2. Size Threshold
3. Circularity Threshold

17. DISCUSSION (Cont.)
Binary
• The tracks are usually having lower brightness than the defects (more dark). The
image is converted to binary image (Black and white)
All pixels having an illumination greater than the threshold value replaced with the value of 1
while the other pixels with the value of 0 .
• Clearness of the images affected by the illumination number of tracks counted .
• The user has to chose the binary threshold.
Origin image Traditional Microscope Digital Microscope
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18. DISCUSSION (Cont.)
Size threshold
The tracks either have circular
or elliptical shapes.
All objects with less than the
pixel threshold were removed.
(26 pixel minimum, 201 pixel maximum)
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19. DISCUSSION (Cont.)
Circularity
Circularity threshold was settled to 0.75 - 1 (practically) this is sufficient for the detection
of circular and elliptical tracks, and objects having values less than this threshold will not
be considered as tracks.
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The uncounted objects were
surrounded by white lines
𝑅 =
4𝜋. 𝐴𝑟𝑒𝑎
𝑃𝑒𝑟𝑖𝑚𝑒𝑡𝑒𝑟2

20. LIMITATION
• The uniformity of illumination is very important to
reduce the graded black shadows on the image
edges
• The system currently lacks features such as counting
overlapping tracks and calculating their average size
to estimate the average energy
• The binary threshold value entered manually by the
user and still observer dependent

21. FUTURE WORK
• One of the suggested methods to count the overlapping tracks is
to divide the size of the overlapped tracks by the average size of
individual track. This method need to be tested for various
overlapping tracks from different detectors and different
particles’ energies
• An algorithm should be developed to calculate the binary
threshold value automatically.
• Enhance the system to detect tracks under the graded black
edges on image.

22. CONCLUSION
• By using MATLAB® software, an automatic system that can count
the tracks on the CR-39 was developed.
• The system is highly dependent on image clearness. Despite this,
the system showed the ability to count the tracks on different
resolutions (0.3 Megapixel and 2 Megapixel )
• A good ability to find and count elliptical tracks .
• The system takes less than one minute for track counting per
detector .

23. Finally
A new set of CR-39 has exposed
to a photon beam in LINAC unit to
detect the neutron activation
when using megavoltage of 10MV Optically Stimulated Luminescence (OSL)

24. Thank You
Questions

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