Detección automática de colores en imágenes dermatoscópicas de lesiones pigmentadas

En este Trabajo Fin de Grado se expone una técnica que se encarga de la detección automática de colores en imágenes dermatoscópicas de lesiones pigmentadas, basada en un algoritmo orientado a la identificación de pigmentos en este tipo de imágenes, mediante una nueva metodología fundamentada en el empleo de modelos de mezcla Gaussiana. Dicha detección de los colores presentes en las lesiones pigmentadas de imágenes dermatoscópicas es muy importante para determinar el diagnóstico de malignidad de cada una de ellas. Este algoritmo se propone en el artículo: Color identification in dermoscopy images using Gaussian mixture models, escrito por Barata, C., Figueiredo, M. A. T., Celebi, M. E., & Marques, J. S. [1]. El primer paso que lleva a cabo la técnica propuesta en este documento, es el del cálculo de 6 modelos de mezcla Gaussianas, uno para cada uno de los colores dermatoscópicos. Para este propósito, se hace uso de un conjunto de parches, que servirán para el debido entrenamiento del algoritmo. Una vez calculadas las 6 mezclas, ya se puede pasar a la detección y cuantificación de colores para las imágenes que forman parte de la base de datos. Finalmente, los resultados muestran que el método propuesto no es muy eficiente en cuanto a la asignación de colores a los píxeles que componen las lesiones pigmentadas analizadas. Esto se comprueba mediante los parámetros de Sensibilidad (Sen (detección de colores) = 69,40 % y Sen (asignación de colores a los píxeles de las lesiones) = 44,06%), VPP (VPP (detección de colores) = 77,91% y VPP (asignación de colores a los píxeles de las lesiones) = 29,84%) y Exactitud (Acc (detección de colores) = 75,66% y Acc (asignación de colores a los píxeles de las lesiones) = 90,03%).This Bachelor Tesis, shows a technique which undertakes the automatic detection of colors in dermoscopic images of pigmented skin lesions. This technique is based in an algorithm oriented to the identification of colors in this type of images, using a new methodology based on the utilization of Gaussian mixture models. That detection of the colors present in the pigmented lesions of the dermoscopic images, is very important in order to determine the malignancy diagnosis of each lesion. This algorithm is proposed in the article: Color identification in dermoscopy images using Gaussian mixture models, written by Barata, C., Figueiredo, M. A. T., Celebi, M. E., & Marques, J. S. [1]. The first step carried out by the proposed technique, is the calculation of 6 Gaussian mixture models, one for each dermoscopic color. For that purpose, a set of patches is used as a training set for the algorithm. Once the six mixtures have been calculated, it is time to carry out the detection and quantification of colors for the images that belongs to the data base. Finally, the results show that the proposed methos is not very efficient as to the pixel color label evaluation permormed for every analyzed lesion. This is proved by parameters like Sensitivity (Sen (colors detection = 69,40 % y Sen (pixel color label evaluation) = 44,06%), PVV (VPP (colors detection) = 77,91% y VPP (pixel color label evaluation) = 29,84%) y Accuracy (Acc (colors detection) = 75,66% y Acc (pixel color label evaluation) = 90,03%).Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given