Cutaneous leishmaniasis (CL) is a neglected tropical disease that requires novel tools for its understanding, diagnosis, and treatment follow-up. In the cases of other cutaneous pathologies, such as cancer or cutaneous ulcers due to diabetes, optical diffuse reflectance-based tools and methods are widely used for the investigation of those illnesses. These types of tools and methods offer the possibility to develop portable diagnosis and treatment follow-up systems. In this article, we propose the use of a three-layer diffuse reflectance model for the study of the formation of cutaneous ulcers caused by CL. The proposed model together with an inverse-modeling procedure were used in the evaluation of diffuse-reflectance spectral signatures acquired from cutaneous ulcers formed in the dorsal area of 21 golden hamsters inoculated with Leishmanisis braziliensis. As result, the quantification of the model’s variables related to the main biological parameters of skin were obtained, such as: diameter and volumetric fraction of keratinocytes, collagen; volumetric fraction of hemoglobin, and oxygen saturation. Those parameters show statistically significant differences among the different stages of the CL ulcer formation. We found that these differences are coherent with histopathological manifestations reported in the literature for the main phases of CL formation.

International audienceWe present in this paper a method to estimate four significant biological parameters of colon tissue. The interaction of light with colon tissue is modeled by two layers parameterized by biological parameters, which describe optical properties of the colon. This model is reversed using an optimization framework based on genetic algorithms. From a multispectral image of colon, we compute biological parameters of the colon, this noninvasive optical biopsy might lead to better diagnosis of cancer. We present in this paper experimental results analyzing multispectral images of excised colon tissue samples. We analyze the following three categories of colonic tissue: healthy tissue, with a polyp and with cancerous cells

International audienceWe present in this paper the decomposition of human skin absorption spectra with a Non-negative Matrix Factorization method. In doing so, we are able to quantify the relative proportion of the main chromophores present in the epidermis and the dermis. We present experimental results showing that we obtain a good estimate of melanin and hemoglobin concentrations. Our approach has been validated by analyzing the human skin absorption spectra in areas of healthy skin and areas affected by melasma on eight patients

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Skin ulcers (SU) are ones of the most frequent causes of consultation in primary health-care units (PHU) in tropical areas. However, the lack of specialized physicians in those areas, leads to improper diagnosis and management of the patients. There is then a need to develop tools that allow guiding the physicians toward a more accurate diagnosis. Multi-spectral imaging systems are a potential non-invasive tool that could be used in the analysis of skin ulcers. With these systems it is possible to acquire optical images at different wavelengths which can then be processed by means of mathematical models based on optimization 1 approaches. The processing of those kind of images leads to the quantification of the main components of the skin. In the case of skin ulcers, these components could be correlated to the different stages of wound healing during the follow-up of a skin ulcer. This article presents the processing of a skin ulcer multi-spectral image. The ulcer corresponds to Leishmaniasis which is one of the diseases the most prominent in tropical areas. The image processing is performed by means of a light-tissue interaction model based on the distribution of the skin as a semi-infinite layer. The model, together with an optimization approach allows quantifying the main light-absorbing and scattering skin-parameters in the visible and near-infrared range. The results show significant differences between healthy and unhealthy area of the image.