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Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.

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Purpose The quality of a measured distribution of dose delivered against its corresponding radiotherapy plan is routinely assessed by gamma index (GI) and dose–volume histogram (DVH) metrics. Any correlation between error detection rates, as based on either of these approaches, while argued, has never been convincingly demonstrated. The dependence of the strength of correlation between the GI passing rate (γP) and DVH quality assurance (QA) metrics on various elements of the therapy plan has not been systematically investigated. Methods A formal analysis of the relation between γP and DVH metrics has been undertaken, leading to a relationship which may partly approximate γP with respect to the DVH. This relationship was further validated by studying examples of simulated clinical radiotherapy plans and by studying the correlation between γP and the derived relationship using a simple two‐dimensional representations of the planning target volume (PTV) and organs at risk (OAR), where penumbra regions, distance‐to‐agreement tolerances and dose delivery errors were systematically varied. Results It is shown formally that there cannot be any correlation between γP and other commonly applied DVH‐derived QA measures. However, γP may be partly approximated given the planned and measured DVH. The derived γP approximation (the “γ‐slope indicator”) may be clinically useful in some practical cases of radiotherapy plan QA. Conclusions In formal terms, there cannot be any correlation between γP and any common DVH‐calculated patient‐specific measures, with respect to PTV or OAR. However, as demonstrated analytically and further confirmed in our simulation studies, the γP approximation derived in this study (the “γ‐slope indicator”) may in some cases offer a degree of correlation between γP and the PTV and OAR DVH QA metrics in measured and planned patient‐specific dose distributions—which may be potentially useful in clinical practice.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.