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.

The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.

a b s t r a c tSkin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising.

Background and Objectives:The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: 1) a subset selection model based on specific feature groups, 2) a correlation-based subset selection model, and 3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a crossvalidation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions:The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is the find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour-and texturerelated features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimumpath forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. Proposed MethodThe classification process was divided into two phases:  Phase 1: Finding the best feature vector for each class;  Phase 2: Create the final classification system from the best results obtained in the previous phase. Phase 1: Finding the Best Feature VectorThis phase is aimed at finding the best feature vector to represent each of the classes to be considered during classification. To achieve this goal the following steps were accomplished: Decomposition of images into multiple channels (R, G, B, H, S, V, I3a, I3b, and GL);  Application of the discrete wavelet transform (DWT) up to the third level;  Computation of the energy for each sub-band and compose the feature vector; Creation of the SVM classification environment;  Listing of the images used for training and testing;  Evaluation of the best feature vectors. Decomposition of the ImageThe decomposition of the images is the first process the system executes. In this stage an image is decomposed into nine channels, namely: R, G, B, H, S, V, I3a, I3b and GL. Application of the Discrete Wavelet TransformDiscrete Wavelet Transform (TWD) decomposition is applied up to the third level. When an image is decomposed as such it will have ten sub-bands, as illustrated in Figure 3. Note that each sub-band is identified by a number between 1 and 10. Region A1 and sub-bands 8, 9 and 10, are generated by the first level of decomposition of the DWT. Region A2 and sub-bands 5, 6 and 7 refer to the second level of decomposition, and the third level is formed by the sub-bands 1, 2, 3 and 4. Computation of the Energy for Each Sub-bandAfter applying the DWT to the three levels, the energy for each wavelet sub-band is computed. Each value obtained is inserted into a feature vector as the one illustrated in Figure 4. The vector in this figure consists of ten elements, each of them is identified by a number corresponding to the number of the sub-band in Figure 3. The energy value computed for each sub-band is stored in the corresponding vector element.Vector Features 1 2 3 4 5 6 7 8 9 10 Figure 4: Example of the structure of a feature vector. Creation of an SVM Classification EnvironmentThe network architecture used is shown in Figure 5. Note that 10 input elements are used. To each input element is assigned the value of the element of the corresponding characteristic vector. In the hidden layer there are a number of neurons (N) equal to the number of training examples, making the net converge...

Há grandes personalidades femininas na história da computação que tiveram importante atuação nos feitos históricos desta área. Todavia, muitas vezes as suas contribuições são fracamente divulgadas e/ou os créditos dessas contribuições são negados às verdadeiras autoras. Assim, esse artigo propõe o jogo denominado Mundo Bit Byte, criado por um time de meninas de graduação e ensino médio, cuja temática e enredo são baseados em cinco personalidades femininas de destaque na área da Computação. Cada fase do jogo é inspirada na vida de uma dessas mulheres, mostrando, de uma forma lúdica e divertida, as conquistas e outros aspectos relevantes da trajetória da mulher tema da etapa. Uma versão demo do jogo contendo a primeira fase foi avaliada por 234 pessoas de diferentes níveis de escolaridade, gêneros e faixas etárias. Os resultados indicaram que a experiência com a primeira fase do jogo contribuiu para o conhecimento e despertou o interesse dos jogadores em conhecer mais sobre personalidades femininas na história da computação.

Resumo: Neste capítulo, propõe-se uma metodologia híbrida para detectar e extrair os contornos de lesões de pele a partir de imagens, bem como a definição de características usualmente utilizadas no diagnóstico de lesões. O método de segmentação por divisão e união (Split and Merge) foi adotado para detectar a lesão e obter o seu contorno inicial. Em seguida, este contornoé refinado pelo modelo de contorno ativo tradicional. Características da lesão usadas na regra ABCD, são definidas a partir do contorno refinado. Os resultados experimentais indicam que o método propostoé promissor para detectar asáreas com lesão e extrair seus contornos a partir de imagens, mantendo suas características.Palavras-chave: Segmentação de Imagens Médicas, Lesões de Pele, Crescimento de Regiões, Contornos Ativos.Abstract: A hybrid methodology for detecting and extracting skin lesion contours from images as well as the definition of common lesion diagnosis features are presented in this paper. The split and merge segmentation method has been applied for lesion detection and for the extraction of its initial contour. This contour is then adjusted using the traditional active contour model. Using the final contour characteristic features are defined according to the ABCD rule. Experimental results show that the proposed method is promising in detecting ill areas as well as extracting their contours from images, while keeping lesions features.