Specular reflection removal in dermatological RGB images using Blind Source Separation for improved skin disease identification

In this paper, we propose a new method for removing specular reflection from RGB dermatological images to improve skin disease identification. This method is based on the dichromatic reflection model, in which a skin reflectance image is considered as a mixture of diffuse reflection components and a specular reflection component. Based on Blind Source Separation (BSS) and exploiting Artificial Intelligence (AI), the proposed method proceeds in two steps. The first step consists of estimating each of these components separately in a fully blind manner by exploiting BSS methods based on Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF), respectively. By retaining only the diffuse reflection image, we obtain a new image, referred to as a pre-processed image that is free of specular reflection. The second step consists of identifying the skin disease from the new pre-processed image using AI. The performance of our method is evaluated on several databases of real RGB dermatological images of melanoma and naevus , as well as on new artificial dermatological images, and then compared with several existing methods. The obtained results show that the proposed method outperforms the existing methods in terms of specular reflection removal in the first pre-processing step. Furthermore, the contribution of this pre-processing step to the identification of the two diseases ( melanoma and naevus ) was evaluated, and the results show that the exploitation of RGB dermatological images pre-processed by our method significantly improves the identification performance compared to the exploitation of raw RGB dermatological images.