Fuzzy uncertainty in detection and counting the chemically etched tracks

Abstract

In this paper, a hybrid algorithm for analysis of nuclear tracks images captured from the surface of CR-39 detectors is presented. Uncertainty in nuclear track detection and counting problems is interpreted here in fuzzy domain and the track models are defined with fuzzy variables instead on crisp ones. A 3D texture-geometric feature is defined to model the formed nuclear tracks on CR-39 detectors. The model function is then approximated by fitting a quasi-sinc function which its parameters are optimally found using a number of training track samples. A similarity function is then defined for the purpose of track detection. The effects of fuzzy concept, an adaptive contrast enhancement preprocessing phase and the number of training samples in nuclear track detection and counting are finally studied.