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. 2024 Oct 14;14(20):2288.
doi: 10.3390/diagnostics14202288.

Blind Separation of Skin Chromophores from Multispectral Dermatological Images

Mustapha Zokay  1 , Hicham Saylani  1
Affiliations

Blind Separation of Skin Chromophores from Multispectral Dermatological Images

Mustapha Zokay et al. Diagnostics (Basel). .

Abstract

Background/Objectives: Based on Blind Source Separation and the use of multispectral imaging, the new approach we propose in this paper aims to improve the estimation of the concentrations of the main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin), while considering shading as a fully-fledged source. Methods: In this paper, we demonstrate that the use of the Infra-Red spectral band, in addition to the traditional RGB spectral bands of dermatological images, allows us to model the image provided by each spectral band as a mixture of the concentrations of the three chromophores in addition to that of the shading, which are estimated through four steps using Blind Source Separation. Results: We studied the performance of our new method on a database of real multispectral dermatological images of melanoma by proposing a new quantitative performances measurement criterion based on mutual information. We then validated these performances on a database of multispectral dermatological images that we simulated using our own new protocol. Conclusions: All the results obtained demonstrated the effectiveness of our new approach for estimating the concentrations of the skin chromophores from a multispectral dermatological image, compared to traditional approaches that consist of using only the RGB image by neglecting shading.

Keywords: Blind Source Separation; chromophores; deoxyhemoglobin; melanin; multispectral dermatological images; oxyhemoglobin; shading.

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Conflict of interest statement

The authors declare no conflicts of interests.

Figures

Figure 1
Figure 1
Chromophore absorption coefficient [25,39] and the 4 spectral bands of multispectral imaging.
Figure 2
Figure 2
Flowchart illustrating the main steps of our method BCSnmf-Irgb.
Figure 3
Figure 3
Processed dermatological image: (a) Image with specular reflection, (b) Image without specular reflection.
Figure 4
Figure 4
Contributions of chromophores and shading estimated by each of the three methods: (a) BCS-rgb, (b) BCS-Irgb and (c) BCSnmf-Irgb.
Figure 5
Figure 5
Example of dermatological images containing hair.
Figure 6
Figure 6
(a) Simulated contributions of the three chromophores and shading, (b) Resulting RGB dermatological image.
Figure 7
Figure 7
Contributions of the chromophores and shading estimated by each of the three methods: (a) BCS-rgb, (b) BCS-Irgb and (c) BCSnmf-Irgb.
See this image and copyright information in PMC

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