One common approach to separatingmelanin and hemoglobin distribution from a color image is Independent Component Analysis (ICA). In this study, we propose a method based on deep learning to automatically detect suitable areas for successful facial pigmentation analysis. To do that, three deep learning models are utilized for segmentation and localization to offer a candidate region for ICA. The experiment was conducted using cross-polarized facial images selected from 200 subjects, and results showed that the deep learning-guided ICA can effectively identify regions of hyperpigmentation and successfully separate melanin and hemoglobin maps for evaluation.
SignificanceMelanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive.AimWe propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light–tissue interactions. The model is structurally extensible for various light sources and cameras and maintains the input image resolution for medical applications.ApproachA facial image is divided into multiple patches and decomposed into melanin, hemoglobin, shading, and specular maps. The outputs are reconstructed into a facial image by solving the forward problem over skin areas. As learning progresses, the difference between the reconstructed image and input image is reduced, resulting in the melanin and hemoglobin maps becoming closer to their distribution of the input image.ResultsThe proposed approach was evaluated on 30 subjects using the professional clinical system, VISIA VAESTRO. The correlation coefficients for melanin and hemoglobin were found to be 0.932 and 0.857, respectively. Additionally, this approach was applied to simulated images with varying amounts of melanin and hemoglobin.ConclusionThe proposed approach showed high correlation with the clinical system for analyzing melanin and hemoglobin distribution, indicating its potential for accurate diagnosis. Further calibration studies using clinical equipment can enhance its diagnostic ability. The structurally extensible model makes it a promising tool for various image acquisition conditions.
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