ArticleCureus2026
Objective Hair Shaft Diameter Measurement From Trichoscopic Images Using Intensity Profile Analysis.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Hair shaft diameter variability (anisotrichosis) is the most sensitive trichoscopic feature of androgenetic alopecia, yet its objective assessment remains dependent on manual measurements or dedicated image-analysis platforms. This technical report describes a simple and standardized workflow for objective hair shaft morphometry using grayscale intensity profile analysis from conventional trichoscopic images. A trichoscopic image obtained from a 50-year-old woman with female pattern hair loss was analyzed using Fiji (ImageJ). After grayscale conversion and threshold-based binarization, the image was systematically divided into four quadrants, and five free-standing proximal hair shafts were selected from each quadrant. Perpendicular grayscale intensity profiles were generated using the native Plot Profile function, and hair shaft width was estimated by counting consecutive low-intensity profile points below a predefined grayscale threshold of 200 arbitrary units, which was empirically selected for this proof-of-concept analysis. Twenty individual hair shafts were successfully analyzed. The proposed workflow consistently generated well-defined grayscale intensity profiles that enabled objective estimation of hair shaft width using only native Fiji functionality without proprietary software, additional plugins, or programming expertise. This single-image proof-of-concept study demonstrates how native image-analysis functions can be repurposed to transform qualitative dermatologic observations into objective morphometric descriptors while establishing a methodological foundation for future automated hair morphometry and quantitative characterization of hair shaft heterogeneity.
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