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.
Highlights
Image segmentation is a fundamental precursor to quantitative image analysis
Experiments evaluated (i) whether manual segmentation is reproducible within and between examiners (ii) whether manually defining a threshold level offers accurate and reproducible results, and (iii) whether a global thresholding algorithm might perform with similar accuracy, while offering improved reproducibility and efficiency (16 algorithms tested)
Three algorithms (Yen, MaxEntropy, and RenyiEntropy) offered similar accuracy, with improved reproducibility and efficiency. 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
Summary
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. 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. Utilising a fluorescent proxy can allow for the rapid generation of data, since sampling can be nondestructive (photographic) rather than destructive (involving the physical or chemical processing of samples) [2] Such studies can help to expand our understanding of trace evidence dynamics and, inform the nuanced. It has been suggested that manually defining a threshold level may be unreliable, and problematic as part of a reproducible workflow [12,13,14]
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