Integrated deep learning approach for generating cross-polarized images and analyzing skin melanin and hemoglobin distributions.

Cross-polarized images are beneficial for skin pigment analysis due to the enhanced visualization of melanin and hemoglobin regions. However, the required imaging equipment can be bulky and optically complex. Additionally, preparing ground truths for training pigment analysis models is labor-intensive.

Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

Background: Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skin redness.

Methods: This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

Background: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements.

Methods: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images.

Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach.

Background: Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility.

Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.

This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources.

Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution.

Significance: Melanin 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.

Aim: We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions.

Computational Large Field-of-View RGB-D Integral Imaging System.

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays.

Analysis of relation between skin elasticity and the entropy of skin image using near-infrared and visible light sources.

Skin elasticity has been regarded as one of the main indicators of skin condition. Current measurement devices for skin elasticity are mostly expensive for home-use and should contact the skin surface. As a first step to develop improved methods, we focus on the relation between skin elasticity and the entropy of skin images.

The hemodynamic changes during cupping therapy monitored by using an optical sensor embedded cup.

Cupping therapy is one form of alternative medicine that is used widely across the world. Although the applications of cupping therapy including pain relief have a 1000-year history, the therapeutic effect of cupping is still questionable due to a lack of scientific evidence. Therefore, in the present study, we embedded a near-infrared spectroscopic sensor into a suction cup to monitor the hemodynamic changes on the treated site while the hemodynamics at the surrounding tissue of the cup was also simultaneously monitored by another near-infrared spectroscopic sensor.