Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns

ISBN:978-1-4799-8339-1International audienceThis paper introduces a method for characterizing and classifying skin lesions in dermoscopic color images with the goal of detecting which ones are melanoma (cancerous lesions). The images are described by means of the Local Binary Patterns (LBPs) computed on geometrical feature maps of each color component of the image. These maps are extracted from geometrical measurements of the General Adaptive Neighborhoods (GAN) of the pixels. The GAN of a pixel is a region surrounding it and fitting its local image spatial structure. The performance of the proposed texture descriptor has been evaluated by means of an Artificial Neural Network, and it has been compared with the classical LBPs. Experimental results using ROC curves show that the GAN-based method outperforms the classical one and the dermatologists' predictions

Automatic classification of skin lesions using color mathematical morphology-based texture descriptors

SPIE : Society of Photo-Optical Instrumentation EngineersInternational audienceIn this paper an automatic classification method of skin lesions from dermoscopic images is proposed. This method is based on color texture analysis based both on color mathematical morphology and Kohonen Self-Organizing Maps (SOM), and it does not need any previous segmentation process. More concretely, mathematical morphology is used to compute a local descriptor for each pixel of the image, while the SOM is used to cluster them and, thus, create the texture descriptor of the global image. Two approaches are proposed, depending on whether the pixel descriptor is computed using classical (i.e. spatially invariant) or adaptive (i.e. spatially variant) mathematical morphology by means of the Color Adaptive Neighborhoods (CANs) framework. Both approaches obtained similar areas under the ROC curve (AUC): 0.854 and 0.859 outperforming the AUC built upon dermatologists' predictions (0.792)

Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

art. 061104Se proponen diferentes descriptores de textura para la clasificación automática de lesiones cutáneas a partir de imágenes dermoscópicas. Se basan en el análisis de textura de color obtenido de (1) morfología matemática del color (MM) y mapas autoorganizativos de Kohonen (SOM) o (2) patrones binarios locales (LBP), calculados con el uso de barrios adaptativos locales de la imagen. Ninguno de estos dos enfoques necesita un proceso de segmentación anterior. En el primer descriptor propuesto, los barrios adaptativos se utilizan como elementos de estructuración para llevar a cabo operaciones MM adaptables que se combinan aún más mediante el uso de KOhonen SOM; esto se ha comparado con una versión no adaptativa. En la segunda, las vecindades adaptables permiten definir mapas de entidades geométricas, a partir de los cuales se calculan histogramas LBP. Esto también se ha comparado con un enfoque clásico de LBP. Un análisis de las características operativas del receptor de los resultados experimentales muestra que el enfoque adaptativo de LBP basado en la vecindad produce los mejores resultados. Supera a las versiones no adaptativas de los descriptores propuestos y las predicciones visuales de los dermatólogos.S

Automatic diagnosis of melanoma from digital images of melanocytic tumors : Analytical and comparative approaches

Le mélanome est la forme la plus grave de cancer de la peau. Cette thèse a contribué au développement de deux approches différentes pour le diagnostic assisté par ordinateur du mélanome : approche analytique et approche comparative.L'approche analytique imite le comportement du dermatologue en détectant les caractéristiques de malignité sur la base de méthodes analytiques populaires dans une première étape, et en combinant ces caractéristiques dans une deuxième étape. Nous avons étudié l’impacte d’un système du diagnostic automatique utilisant des images dermoscopique de lésions cutanées pigmentées sur le diagnostic de dermatologues. L'approche comparative, appelé concept du Vilain Petit Canard (VPC), suppose que les naevus chez le même patient ont tendance à partager certaines caractéristiques morphologiques ainsi que les dermatologues identifient quelques groupes de similarité. VPC est le naevus qui ne rentre dans aucune de ces groupes, susceptibles d'être mélanome.Melanoma is the most serious type of skin cancer. This thesis focused on the development of two different approaches for computer-aided diagnosis of melanoma: analytical approach and comparative approach. The analytical approach mimics the dermatologist’s behavior by first detecting malignancy features based on popular analytical methods, and in a second step, by combining these features. We investigated to what extent the melanoma diagnosis can be impacted by an automatic system using dermoscopic images of pigmented skin lesions. The comparative approach, called Ugly Duckling (UD) concept, assumes that nevi in the same patient tend to share some morphological features so that dermatologists identify a few similarity clusters. UD is the nevus that does not fit into any of those clusters, likely to be suspicious. The goal was to model the ability of dermatologists to build consistent clusters of pigmented skin lesions in patients

Ugly duckling sign as a major factor of efficiency in melanoma detection

IMPORTANCE Understanding the contribution of the ugly duckling sign (a nevus that is obviously different from the others in a given individual) in intrapatient comparative analysis (IPCA) of nevi may help improve the detection of melanoma. OBJECTIVES To assess the agreement of dermatologists on identification of the ugly duckling sign and estimate the contribution of IPCA to the diagnosis of melanoma. DESIGN, SETTING, AND PARTICIPANTS The same 2089 digital images of the nevi of a sample of 80 patients (mean age, 42 years [range, 19-80 years]; 33 men and 47 women), as well as 766 dermoscopic images from a subset of 30 patients (mean age, 40 years [range, 21-75 years]; 12 men and 18 women), were randomly presented to the same 9 dermatologists for blinded assessment from September 22, 2011, to April 1, 2013. The first experiment was designed to mimic an IPCA situation, with images of all nevi of each patient shown to the dermatologists, who were asked to identify ugly duckling nevi (UDN). The second experiment was designed to mimic a lesion-focused analysis to identify morphologically suspicious nevi. Data analysis was conducted from November 1, 2012, to June 1, 2013. MAIN OUTCOMES AND MEASURES Number of nevi labeled UDN and morphologically suspicious nevi, specificity of lesion-focused analysis and IPCA, and number of nevi identified for biopsy. RESULTS Of the 2089 clinical images of nevi from 80 patients (median number of nevi per patient, 26 [range, 8-81]) and 766 dermoscopic images (median number of nevi per patient, 19 [range, 8-81]), all melanomas were labeled UDN and as morphologically suspicious nevi by the 9 dermatologists. The median number of UDN detected per patient was 0.8 among the clinical images of nevi (mean, 1.0; range, 0.48-2.03) and 1.26 among the dermoscopic images (mean, 1.4; range, 1.00-2.06). The propensity to consider more or fewer nevi as having ugly duckling signs was independent of the presentation (clinical or dermoscopic). The agreement among the dermatologists regarding UDN was lower with dermoscopic images (mean pairwise agreement, 0.53 for clinical images and 0.50 for dermoscopic images). The specificity of IPCA was 0.96 for clinical images and 0.95 for dermoscopic images vs 0.88 and 0.85, respectively, for lesion-focused analysis. When both IPCA and lesion-focused analyses were used, the number of nevi considered for biopsy was reduced by a factor of 6.9 compared with lesion-focused analysis alone. CONCLUSIONS AND RELEVANCE Intrapatient comparative analysis is of major importance to the effectiveness of the diagnosis of melanoma. Introducing IPCA using the ugly duckling sign in computer-assisted diagnosis systems would be expected to improve performance