1. Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation 
Elena Bernardis, Leslie Castelo-Soccio 
Journal of Investigative Dermatology Symposium Proceedings 
Volume 19, Issue 1, Pages S34-S40 (January 2018)
DOI: /j.jisp
Copyright © 2017 The Authors Terms and Conditions

2. Figure 1
Automated SALT score results. Results for a sample patient with four image views. For each set, we show (a) original input image, (b) images manually annotated by an expert, and (c) machine output using the texture classification algorithm presented in this article. Regions where hair is completely absent are marked in darker orange. Regions with normal hair density are marked in green. For each image, we also indicate its SALT score, that is, the percentage of abnormal hair density, and the covering (C) and shape match (SM) used in the evaluations. SALT, Severity of Alopecia Tool.
Journal of Investigative Dermatology Symposium Proceedings  , S34-S40DOI: ( /j.jisp )
Copyright © 2017 The Authors Terms and Conditions

3. Figure 2
Results and comparisons of algorithm to the previous work of Gibbons et al. (1986) and to texture analysis without context. Top of head image views of four different patients. (a) Input image. (b) Input image manually labeled by clinician. (c) Results of intensity histogram thresholding (small box) reproducing prior work of Gibbons et al. (1986), as well as predicted outlines of normal and abnormal scalp. (d) Outlines of normal and abnormal performed using texture analysis without a hair specific reference set. (e) Our algorithm results with hair-specific reference set. Green lines enclose regions of normal hair density and orange lines enclose areas with no hair or scalp.
Journal of Investigative Dermatology Symposium Proceedings  , S34-S40DOI: ( /j.jisp )
Copyright © 2017 The Authors Terms and Conditions

4. Figure 3
Quantitative evaluation comparing SALT score, covering, and shape metrics. Graphs depict comparison of (a) Gibbons et al. (1986) method and (b) texture classification without reference set versus (c) our algorithm. In each graph, we plot the covering (y-axis) versus the shape match (x-axis), where the former measures how well the regions overlap with the manually annotated ones and the latter measures their shape resemblance. SALT score comparison with manual is depicted with color, where green is SALT score of 0 and red is SALT score of 100. Size of each point is inversely proportional to the error in SALT score between algorithm and clinician. SALT, Severity of Alopecia Tool.
Journal of Investigative Dermatology Symposium Proceedings  , S34-S40DOI: ( /j.jisp )
Copyright © 2017 The Authors Terms and Conditions

5. Figure 4
User interface output for manually annotated alopecia areata image. Example of annotated image depicting areas of bald/no hair scalp in lavender, normal-density hair in yellow, low-density hair in red, and “clinical scalp or bald” showing where hair is overlying bald scalp in pink.
Journal of Investigative Dermatology Symposium Proceedings  , S34-S40DOI: ( /j.jisp )
Copyright © 2017 The Authors Terms and Conditions

6. Figure 5
Extracting hair ‘textons’ and creating a hair texture reference set. (a) Depiction of input images divided into small neighborhood of pixels. (b) Selection of pixel intensities used to extract (c) textons from individual images. (d) Texton reference set created by pooling the textons from c.
Journal of Investigative Dermatology Symposium Proceedings  , S34-S40DOI: ( /j.jisp )
Copyright © 2017 The Authors Terms and Conditions