Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation

Abstract

Quantifying alopecia areata in real time has been a challenge for clinicians and investigators. Although several scoring systems exist, they can be cumbersome. Because there are more clinical trials in alopecia areata, there is an urgent need for a quantitative system that is reproducible, standardized, and simple. In this article, a computer imaging algorithm to recreate the Severity of Alopecia Tool scoring system in an automated way is presented. A pediatric alopecia areata image set of four view-standardized photographs was created, and texture analysis was used to distinguish between normal hair and bald scalp. By exploiting local image statistics and the similarity of hair appearance variations across the pediatric alopecia examples, we then used a reference set of hair textures, derived from intensity distributions over very small image patches, to provide global context and improve partitioning of each individual image into areas of different hair densities. This algorithm can mimic a Severity of Alopecia Tool (score) and may also provide more information about the continuum of changes in density of hair seen in alopecia areata.