Simple detection of absorption change in skin tissue using simulated spectral reflectance database
Introduction
Importance on optical investigation of human skin has grown by leaps and bounds over the years [1], [2], [3], [4], [5], [6]. Through the analysis of spectral reflectance, knowledge of various skin parameters can be obtained regarding skin conditions such as color, diseases and aging. It has been generally recognized that alteration in the spectral reflectance is mostly influenced by absorption and scattering of light in skin as they can be approximately associated to the presence of chromophores such as melanin and hemoglobin, and the tissue morphology. As various skin diseases and conditions govern modification in the chromophores and tissue structure, it results in characterizing the spectral reflectance [7], [8], [9], [10]. Thus, the analysis of skin spectral reflectance became one of the principal research elements in dermatology and cosmetology. Most recently, the skin spectroscopic technique is extended to application for monitoring of physiological condition and health status [11], [12]. This kind of application is considered to be advantageous for the remote or on-line inspection systems, especially in the present serious situations of infectious diseases. To realize such systems, the spectral reflectance analysis should be made in the simple, easy to use, and costless manner. However, in general it is quite difficult to find simply and directly relation between skin tissue conditions and measured spectral reflectance, although various methods have been explored to approach this subject [5], [6], [7], [9], [13], [14], [15].
Among the methods, multi-variate analysis of reflectance spectra is probably the most typical approach which has been studied by many groups of researchers in various ways including works in the articles [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [13], [14]. Imai et al. [16] proposed the use of principal component analysis for the assessment of melanin and hemoglobin concentration through image processing. For the same purpose, Tsumura et al. [17] introduced the method of independent component analysis in color images of human face. Shimada et al. [18], [19], [20] presented the applicability of multiple regression analysis (MRA) to evaluating the concentration of melanin and blood in human skin on the basis of modified Beer-Lambert law [21], [22], [23]. This method suffers from non-linearity between chromophore concentrations and corresponding regression coefficients, which was proficiently handled by Nishidate et al. [24], [25] through use of conversion vectors. Konishi et al. [26] and Spigulis et al. [27] also reported the use of MRA for chromophore distribution imaging. These approaches are likely to provide useful results if their regression models are appropriately designed for analysis. However, the models and regression coefficients should be re-constructed in case of different kinds and amounts of changes in the skin conditions. From this point of view, they seem disadvantageous for system extensibility to general purposes including on-line skin monitoring.
Some other methods include solution of the radiative transfer equation with auxiliary function method [28], [29] which has some limitation in application for various skin conditions, and Kubelka-Munk theory [30] which is limited to homogeneous skin tissue model. Zhang et al. [31], [32] presented the idea of using library based spectral fitting for determination of optical properties through the diffusion model. They presented reasonable estimation of nine skin parameters by genetic algorithms, but their skin model consisted of epidermis and dermis layers only. These models generally hold in limited ranges of absorption and scattering changes, owing to their approximation models employed. Thus, they are considered somehow complicated for finding simply change in skin conditions from measured spectra.
As the absorption coefficient corresponds to a product of molar extinction coefficient and molar concentration of chromophore, and the scattering coefficient corresponds to morphology of tissue and cells, these coefficients are considered as key factors that regulate a reflectance spectrum under a certain skin condition. As a flexible approach to obtain various spectral reflectance curves with given values of absorption and scattering coefficients, Monte Carlo method for layered tissue model [33] is well known and widely employed. Meglinski and Matcher [34], [35] reported simulated reflectance spectra using a seven-layered tissue model which provided usefulness for investigating absorption and scattering coefficients in each of layers, but they assumed wavelength independency for scattering coefficients. On the basis of this report, Maeda et al. [36] developed the nine-layered skin tissue model with wavelength-dependent skin parameters (absorption coefficient, scattering coefficient and anisotropy scattering parameter) together with refractive index and thickness of skin layers. This model generated reasonably similar simulated spectra in accordance with measured spectra. By using this approach, we then presented the spectral fitting method for estimation of absorption and scattering coefficients [37]. In this model, we found that a simulated spectrum becomes similar to the measured one through the adjustment of five parameters. However, this adjustment requires the manual iteration process for finding reasonable values for the five parameters. This is quite time consuming and unstable process for obtaining a simulated spectrum very close to the measured spectrum.
In our previous work [38], we proposed the use of a database consisting of various simulated spectra for estimating the absorption and scattering coefficients of a measured spectrum, in which a simulated spectrum that closely resembles with the measured spectrum is extracted out from the spectral database. The absorption and scattering coefficients associated with the extracted spectrum are regarded as estimated absorption and scattering coefficients of the measured spectrum. In the study, we introduced the moderate grouping method [39] for changing values of absorption and scattering coefficients in the nine layers. By this method, we constructed the spectral reflectance database in a reasonable size while utilizing the merit of having nine layers in skin tissue model, and demonstrated possibilities for estimation of absorption and scattering coefficients by simulation study.
The relation between the absorption coefficient and chromophore concentration is clinically proven, and it is regarded as predominantly consequential in both skin care and diagnostics. The relation between the scattering coefficient and tissue morphology (for example, tissue structure, cell structure, and fiber composition such as collagen and elastin) is yet to be clearly identified for clinical usage. For this reason, many researchers have made much effort to mainly focus on evaluating change in the concentration of chromophores such as melanin and hemoglobin (corresponding to absorption change) with their respective techniques [16], [17], [18], [19], [20], [24], [25], [26], [27], which provide useful information on various skin conditions including cancers, pigmentation and tissue metabolism. So, we consider that the database method has much potential to become an effective means for analysis of chromophores concentration, even if the database is used for estimation of only the absorption coefficients in skin tissue.
In this paper, we propose the use of absorption-oriented database which consists of simulated spectra having variation only in absorption coefficients, while restraining the other skin parameters from changing. Although the reflectance spectrum is influenced by various skin parameters like absorption and scattering coefficients and anisotropy factor, refractive index, and thickness, we employ a way to grasp dominantly the absorption change from the spectrum in the constraint of no change on the other parameters. This choice is expected to be effective for simple detection of absorption change in skin tissue. In order to investigate experimentally the feasibility of the proposed method, we performed sun exposure (suntan) experiment for detecting change in melanin concentration or the absorption coefficient in epidermal layer, and pressure-cuff occlusion and hot water immersion experiments for detecting change in hemoglobin concentration or the absorption coefficient in dermal and subcutaneous layers. To discuss the usefulness of the proposed method, we compared the tendency of estimated parameters with those obtained by the MRA method.
Section snippets
Nine-layered skin tissue model and Monte Carlo simulation
Human skin is known to have a complex structure as can be seen in Fig. 1(a). Generally, a skin tissue model is expressed by three layers which are epidermis, dermis and subcutaneous layers. However, this model comes in short in mimicking various skin conditions aptly. One of such examples is that melanin in epidermis and hemoglobin in dermis have inhomogeneous distribution along the vertical direction in skin tissue. We first noticed the seven-layered model which was developed in the preceding
Method of multiple regression analysis
To assert the effectiveness for analyzing absorption change in skin tissue by the database (DB) method, we also performed multiple regression analysis for the measured spectra obtained by experiments conducted in Section 4. Here, we followed the method of Nishidate et al. [24]. According to the modified Lambert-Beer law [20], [21], [22], an absorbance spectrum A(λ) = -log10 R(λ) can be expressed as,where C and ε(λ) are the molar concentration and molar
Experimental setup
To measure a reflectance spectrum, we used the experimental system shown in Fig. 3 [35]. Light from a halogen lamp (Hayashi-Repic, Tokyo, LA-150UX), which covers a visible wavelength range, was focused through a light guide and lens in such a manner that it produces a spot of 4 mm on the surface of a measured sample. Diameter and focal length of lens were 54 and 100 mm, respectively. The diffuse light reflected from the sample, which was placed at a sample port of an integrating sphere
Influence of g, n, and t
In this study, we fixed the values of g, n, and t to be constant for the construction of the database. However, variation in the three parameters have effects on the skin reflectance spectra, because g and n are related to the scattering or reduced scattering properties and t is also to both absorption and scattering properties in terms of the light propagation length. The values of g, n, and t used in this study almost follow the published results of many researchers [40], [51], [55], [56].
Conclusion
We have described the simple construction of absorption-oriented database, in which only absorption coefficients are varied with keeping fixed values in the other parameters, and intended for better detection of the absorption change in skin tissue. Then, we presented the usefulness of this database by performing the three experiments of the suntan, pressure-cuff occlusion and hot water immersion on the forearm of human subjects. In all of these experiments primarily the absorption change due
Disclosures
The authors have no relevant financial interests in this article and no other potential conflicts of interest to disclose.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 20K04513.
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