DETECÇÃO DE LESÕES EM MAMOGRAFIAS ATRAVÉS DA ASSIMETRIA DAS MAMAS E EXTRAÇÃO DE CARACTERÍSTICAS COM ÍNDICE DE GETIS-ORD

O câncer de mama é aquele que tem início nas células das mamas. A principal forma de prevençãoe diagnóstico precoce é através de exames de mamografia. Este trabalho tem como objetivo principalapresentar uma metodologia de auxílio à detecção de lesões em mamografias a partir da determinação de regiões suspeitas por nível de simetria. Técnicas de Processamento de Imagem foram usadas para preparar as mamografias e, em seguida, o nível de simetria entre a mama esquerda e a direita foi medido com coeficiente de correlação cruzada e distância euclidiana. O índice de Getis-Ord na sua forma geral foi usado para extrair características das imagens para treinar uma Máquina de Vetores de Suporte que classificouregiões das mamografias em lesão e não lesão. A metodologia, de modo geral, apresentou 80,11% de sensibilidade, 84,41% de especificidade e 84,38% de acurácia.Palavras-chave: Câncer de mama. Mamografia. Coeficiente de correlação cruzada. Distância euclidiana. Índice de Getis-Ord. Máquina de vetores de suporte. LESION DETECTION IN MAMMOGRAMS THROUGH THE ASYMMETRY OF THEBREASTS AND FEATURE EXTRACTION WITH INDEX GETIS-ORDAbstract: Breast cancer is one that starts in the cells of the breast. The main form of prevention and early diagnosis is through mammograms. This work has as main goal to present a methodology to aid in the detection of lesions on mammograms from the determination of suspicious regions by level of symmetry. Image processing techniques were used to prepare the mammograms and then the degree of symmetry between left and right breasts was measured using cross-correlation coefficient and Euclidean distance. The index Getis-Ord was used to extract features from images to train a Support Vector Machine which classified regions of mammograms in lesion and non-lesion. The methodology, in general, showed 80.11% sensitivity, 84.41% specificity and 84.38% accuracy.Keywords: Breast cancer. Mammography. Cross-correlation coefficient. Euclidean distance. Index Getis-Ord. Support vector machine. DETECCIÓN DE LESIONES EN LAS MAMOGRAFÍAS A TRAVÉS DE LA ASIMETRÍA DE LAS MAMAS Y EXTRACCIÓN DE CARACTERÍSTICAS CON EL ÍNDICE GETIS-ORDResumen: El cáncer de mama comienza en las células de los senos. La principal forma de prevención y diagnóstico precoz es a través de mamografías. Este trabajo tiene como objetivo principal presentar una metodología para ayudar en la detección de lesiones en las mamografías a partir de la determinación de las regiones sospechosas por nivel de simetría. Técnicas de procesamiento de imágenes se utilizaron para preparar las mamografías y luego el nivel de simetría entre el pecho izquierdo y derecho se midió utilizando el coeficiente de correlación cruzada y la distancia euclidiana. El índice Getis-Ord se utilizó para extraer características de las imágenes para formar una máquina de vectores de soporte que las regiones clasificadasde mamografías en lesión y no la lesión. La metodología, en general, mostró 80,11% de sensibilidad, especificidad 84,41% y 84,38% de precisión.Palabras clave: Cáncer de mama. Mamografía. Coeficiente de correlación cruzada. Distancia euclídea. Índice Getis-Ord. Máquina de vectores soporte

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora