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A Comparison of Thresholding Methods for Forensic Reconstruction Studies Using Fluorescent Powder Proxies for Trace Materials.
Levin, Emma A; Morgan, Ruth M; Griffin, Lewis D; Jones, Vivienne J.
Afiliação
  • Levin EA; Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, U.K.
  • Morgan RM; Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, U.K.
  • Griffin LD; Environmental Change Research Centre, Department of Geography, University College London, Pearson Building, Gower Street, London, WC1E 6BT, U.K.
  • Jones VJ; Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, U.K.
J Forensic Sci ; 64(2): 431-442, 2019 Mar.
Article em En | MEDLINE | ID: mdl-30359482
ABSTRACT
Image segmentation is a fundamental precursor to quantitative image analysis. At present, no standardised methodology exists for segmenting images of fluorescent proxies for trace evidence. Experiments evaluated (i) whether manual segmentation is reproducible within and between examiners (with three participants repeatedly tracing three images) (ii) whether manually defining a threshold level offers accurate and reproducible results (with 20 examiners segmenting 10 images), and (iii) whether a global thresholding algorithm might perform with similar accuracy, while offering improved reproducibility and efficiency (16 algorithms tested). Statistically significant differences were seen between examiners' traced outputs. Manually thresholding produced good accuracy on average (within ±1% of the expected values), but poor reproducibility (with multiple outliers). Three algorithms (Yen, MaxEntropy, and RenyiEntropy) offered similar accuracy, with improved reproducibility and efficiency. Together, these findings suggest that appropriate algorithms could perform thresholding tasks as part of a robust workflow for reconstruction studies employing fluorescent proxies for trace evidence.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article