Hyperspectral imaging-based erythema classification in atopic dermatitis.

BACKGROUND/PURPOSE: Among the characteristics that appear in the epidermis of the skin, erythema is primarily evaluated through qualitative scales, such as visual assessment (VA). However, VA is not ideal because it relies on the experience and skill of dermatologists. In this study, we propose a new evaluation method based on hyperspectral imaging (HSI) to improve the accuracy of erythema diagnosis in clinical settings and investigate the applicability of HSI to skin evaluation. METHODS: For this study, 23 subjects diagnosed with atopic dermatitis were recruited. The inside of the right arm is selected as the target area and photographed using a hyperspectral camera (HS). Subsequently, based on the erythema severity visually assessed by a dermatologist, the severity classification performance of the RGB and HS images is compared. RESULTS: Erythema severity is classified as high when using (i) all reflectances of the entire HSI band and (ii) a combination of color features (R of RGB, a* of CIEL*a*b*) and five selected bands through band selection. However, as the number of features increases, the amount of calculation increases and becomes inefficient; therefore, (ii), which uses only seven features, is considered to perform classification more efficiently than (i), which uses 150 features. CONCLUSION: In conclusion, we demonstrate that HSI can be applied to erythema severity classification, which can further increase the accuracy and reliability of diagnosis when combined with other features observed in erythema. Additionally, the scope of its application can be expanded to various studies related to skin pigmentation.

Skin color classification of Koreans using clustering.

BACKGROUND/PURPOSE: Skin color is used as an index for diagnosing and predicting skin irritation, dermatitis, and skin conditions because skin color changes based on various factors. Therefore, a new method for consistently and accurately evaluating skin color while overcoming the limitations of the existing skin color evaluation method was proposed, and its usefulness was demonstrated. METHODS: Skin color was quantified using the RGB (Red, Green, Blue), HSV (Hue Saturation Value), CIELab, and YCbCr color spaces in the acquired Korean skin images, which were classified through clustering. In addition, the classification performances of the existing visual scoring method and the proposed skin color classification method were compared and analyzed using multinomial logistic regression, support vector machine, K-nearest neighbor, and random forest. RESULTS: After quantifying the skin color through the color space conversion of the skin image, the skin color classification performance according to the number of quantified features and the classifier was verified. In addition, the usefulness of the proposed classification method was confirmed by comparing its classification performance with that of the existing skin color classification method. CONCLUSION: In this study, a method was proposed to objectively classify skin color values quantified from skin images of Koreans acquired using a digital camera through clustering. To verify the proposed method, its classification performance was compared with that of the existing classification method, and an optimized classification method was presented for the classification of Korean skin color. Thus, the proposed method can objectively classify skin color and can be used as a cornerstone in research to quantify skin color and establish objective classification criteria.