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Programs in Physics & Physical Chemistry



Manuscript Title: TRIAC II. A MatLab code for track measurements from SSNT detectors
Authors: D. L. Patiris, K. Blekas, K. G. Ioannides
Program title: TRIAC II
Catalogue identifier: ADZC_v1_0 Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 177(2007)329
Programming language: MATLAB.
Computer: Pentium III, 600 MHz Installations: MATLAB 7.0.
Operating system: Windows XP.
RAM: 256 MB
Word size: 32 bits
Keywords: Solid state nuclear tracks detectors, automatic track counting, image analysis, alphaparticle radioactivity, radon dosimetry.
PACS: 29.40Wk, 29.40-n, 29.85+c, 92.20Td, 93.85Np, 29.30Ep, 29.90+r, 87.53Pb, 87.53Qc.
Classification: 21, 21.1.
Nature of problem: Following the passage of a charged particle (protons and heavier) through a Solid State Nuclear Track Detector (SSNTD), a damage region is created, usually named latent track. After the chemical etching of the detectors in aqueous NaOH or KOH solutions, latent tracks can be sufficiently enlarged (with diameters of 1 μm or more) to become visible under an optical microscope. Using the appropriate apparatus, one can record images of the SSNTD s surface. The shapes of the particle's tracks are strongly dependent on their charge, energy and the angle of incidence. Generally, they have elliptical shapes and in the special case of vertical incidence, they are circular. The manual counting of tracks is a tedious and time-consuming task. An automatic system is needed to speed up the process and to increase the accuracy of the results.
Solution method: TRIAC II is based on a segmentation method that groups image pixels according to their intensity value (brightness) in a number of grey level groups. After the segmentation of pixels, the program recognizes and separates the track from the background, subsequently performing image morphology, where oversized objects or objects smaller than a threshold value are removed. Finally, using the appropriate Hough transform technique, the program counts the tracks, even those which overlap and classifies them according to their shape parameters and brightness.
Unusual features: This program has been tested with images of CR-39 detectors exposed to alpha particles. Also, in low contrast images with few or small tracks, background pixels can be recognized as track pixels. To avoid this problem the brightness of the background pixels should be sufficiently higher than that of the track pixels.
Additional comments: This program requires the Matlab Statistical Toolbox and the Image Processsing Toolbox to be installed.
Running time: The analysis of an image with a PC (Intel Pentium III processor running at 600MHz) requires 2 to 10 minutes, depending on the number of observed tracks and the digital resolution of the image.