SPECTRAL ANALYSIS AND UNSUPERVISED SVM CLASSIFICATION FOR SKIN HYPER-PIGMENTATION CLASSIFICATION

International audienceData reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning

Multi-scale analysis of skin hyper-pigmentation evolution

International audienceIn this paper, we use statistical inference and muti-spectral images to quantify the evolution of skin hyper-pigmentation lesions under treatment. We show that statistical inference allows getting change maps of the disease which can be useful for dermatologists to analyze the disease evolution. Indeed, a local change map is obtained by computing the deviation between two multi-spectral images in a region of interest (ROI). Then, we normalize the obtained map and develop a statistical inference framework to quantify the changes. Finally, we propose a criterion that integrates change maps in order to quantify the treatment efficacy on a patient

Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images

International audienceClinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy

MULTI-SPECTRAL IMAGE ANALYSIS FOR SKIN PIGMENTATION CLASSIFICATION

International audienceIn this paper, we compare two different approaches for semi- automatic detection of skin hyper-pigmentation on multi- spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation

Skin lesion evaluation from multispectral images

During evaluation of skin disease treatments, dermatologists have to clinically measure the pathology severity of each patient during treatment periods. Such a process is sensitive to intra- and inter- dermatologist diagnosis. To make this severity measurement more robust we propose to use image processing to quantify the pathology severity. We focus on a hyperpigmentation disorder called melasma. During a treatment period, multispectral images are taken on patients receiving the same treatment. After co-registration and classification steps, we propose algorithms to measure the intensity, the size and the homogeneity evolution of the pathological area. Obtained results are compared with a dermatologist diagnosis using statistical tests on a full clinical study

Assessing skin lesion evolution from multispectral image sequences

During the evaluation of skin disease treatments, dermatologists have to clinically measure the evolution of the pathology severity of each patient during treatment periods. Such a process is sensitive to intra- and inter- dermatologist diagnosis. To make this severity measurement more objective we quantify the pathology severity using a new image processing based method. We focus on a hyperpigmentation disorder called melasma. During a treatment period, multispectral images are taken on patients receiving the same treatment. After co-registration and segmentation steps, we propose an algorithm to measure the intensity, the size and the homogeneity evolution of the pathological areas. Obtained results are compared with a dermatologist diagnosis using statistical tests on two clinical studies containing respectively 384 images from 16 patients and 352 images from 22 patients.This research report is an update of the report 8136. It describes methods and experiments in more details and provides more references.Lors de l'évaluation des traitements des maladies de peau, les dermatologues doivent mesurer la sévérité de la pathologie de chaque patient tout au long d'une période de traitement. Un tel procédé est sensible aux variations intra- et inter- dermatologues. Pour rendrecette mesure de sévérité plus robuste, nous proposons d'utiliser l'imagerie spectrale. Nous nous concentrons sur une pathologie d'hyperpigmentation cutanée appelée mélasma. Au cours d'une période de traitement, des images multispectrales sont acquises sur une population de patients sous traitement. Après des étapes de recalage des séries temporelles d'images et de classification des régions d'intérêt, nous proposons une méthodologie permettant de mesurer, dans le temps, la variation de contraste, de surface et d'homogénéité de la zone pathologique pour chaque patient. Les résultats obtenus sont comparés à un diagnostique clinique à l'aide de tests statistiques réalisés sur une étude clinique complète.Ce rapport de recherche est un complément du rapport de recherche 8136, afin de compléter la bibliographie, et de décrire plus en détail les méthodes et résultat

Transcriptional Profiling Shows Altered Expression of Wnt Pathway– and Lipid Metabolism–Related Genes as Well as Melanogenesis-Related Genes in Melasma

Melasma is a commonly acquired hyperpigmentary disorder of the face, but its pathogenesis is poorly understood and its treatment remains challenging. We conducted a comparative histological study on lesional and perilesional normal skin to clarify the histological nature of melasma. Significantly, higher amounts of melanin and of melanogenesis-associated proteins were observed in the epidermis of lesional skin, and the mRNA level of tyrosinase-related protein 1 was higher in lesional skin, indicating regulation at the mRNA level. However, melanocyte numbers were comparable between lesional and perilesional skin. A transcriptomic study was undertaken to identify genes involved in the pathology of melasma. A total of 279 genes were found to be differentially expressed in lesional and perilesional skin. As was expected, the mRNA levels of a number of known melanogenesis-associated genes, such as tyrosinase, were found to be elevated in lesional skin. Bioinformatics analysis revealed that the most lipid metabolism-associated genes were downregulated in lesional skin, and this finding was supported by an impaired barrier function in melasma. Interestingly, a subset of Wnt signaling modulators, including Wnt inhibitory factor 1, secreted frizzled-related protein 2, and Wnt5a, were also found to be upregulated in lesional skin. Immunohistochemistry confirmed the higher expression of these factors in melasma lesions

Classification of skin hyper-pigmentation lesions with multi-spectral images

According to clinical protocols, skin diseases are quantified by dermatologists throughout a treatment period, and then a statistical test on these measures allows to evaluate a treatment efficacy. The first step of this process it to classify pathological interest areas. This task is challenging due to the high variability of the images in one clinical data set. In this report, we first review algorithms that exist in the literature and adapt them to our problem. Then we choose the more appropriate algorithm to design a classification strategy. Thereby, we propose to use data reduction combined with SVM to do a first classification of the disease. Then we associate the obtained classification map with a segmentation map in an "interactive classification tool" in order to compromise between operator dependency and algorithm robustness

Détection de volets pour l'indexation de vidéo par le contenu

Nous nous intéressons au partitionnement temporel de vidéo, qui est une étape nécessaire dans l'analyse du contenu d'un document audiovisuel en vue de son indexation et de son exploitation. L'objectif est plus particulièrement la détection de volets qui sont des transitions progressives fréquemment utilisées dans le montage de vidéo et le changement de scènes. La difficulté de ce problème réside dans la grande variété de ces effets spéciaux, tant du point de vue géométrique (volet horizontal, diagonal, vertical, zoom), que de l'effet généré (frontière franche, graduelle, enroulement), qui complique notablement leurs détections. L'originalité de la méthode proposée consiste à exploiter la distribution spatiale des pixels non conformes au mouvement dominant estimé entre paires d'images successives. Les résultats expérimentaux obtenus sur de nombreux exemples réels montrent d'une part la capacité de la méthode à détecter une grande variété de volets, et d'autre part à être robuste aux fausses alarmes dans le cas de scènes complexes