Paper
3 March 2011 Visual cues do not improve skin lesion ABC(D) grading
Matteo Zanotto, Lucia Ballerini, Ben Aldridge, Robert B. Fisher, Jonathan Rees
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Abstract
In this work evidence is presented supporting the hypothesis that observers tend to evaluate very differently the same properties of given skin-lesion images. Results from previous experiments have been compared to new ones obtained where we gave additional prototypical visual cues to the users during their evaluation trials. Each property (colour, colour uniformity, asymmetry, border regularity, roughness of texture) had to be evaluated on a 0-10 range, with both linguistic descriptors and visual references at each end and in the middle (e.g. light/medium/dark for colour). A set of 22 images covering different clinical diagnoses has been used in the comparison with previous results. Statistical testing showed that only for a few test images the inclusion of the visual anchors reduced the variability of the grading for some of the properties. Despite such reduction, though, the average variance of each property still remains high even after the inclusion of the visual anchors. When considering each property, the average variance significantly changed for the roughness of texture, where the visual references caused an increase in the variability. With these results we can conclude that the variance of the answers observed in the previous experiments was not due to the lack of a standard definition of the extrema of the scale, but rather to a high variability in the way observers perceive and understand skin-lesion images.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matteo Zanotto, Lucia Ballerini, Ben Aldridge, Robert B. Fisher, and Jonathan Rees "Visual cues do not improve skin lesion ABC(D) grading", Proc. SPIE 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, 79660U (3 March 2011); https://doi.org/10.1117/12.878115
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Cited by 4 scholarly publications.
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KEYWORDS
Visualization

Skin

Melanoma

Dermatology

Diagnostics

Human-machine interfaces

Data analysis

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