Home Itens selecionados Provedores de dados OAI Créditos Sobre Ajuda

			Busca avançada
	Provedor de dados Acta biol.Colomb. (1) Anais da ABC (AABC) (1) Autor ANDRADE,ALEXSANDRA O. (1) DÍAZ,GLORIA (1) HERNÁNDEZ-TAMAMES,JUAN ANTONIO (1) LOPES,JULIANE G. (1) MAGALHÃES,ROBSON S. (1) MALPICA,NORBERTO (1) MELO,CLÊNIA A.O. DE (1) MOLINA,VICENTE (1) ROMERO,EDUARDO (1) TRINDADE,ROQUE M.P. (1) Mais... Palavra-chave Pattern classification (2) Arbuscular mycorrhizal fungi spores (1) Artificial neural network (1) Circle Hough transform (1) DARTEL (1) Image preprocessing (1) Neurodegenerative disease (1) SPM (1) Semi-automated counting (1) Structural MRI (1) Support vector machines (1) VBM (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2019 (1) 2010 (1) País Brazil (1) Colombia (1) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES Acta biol.Colomb. DÍAZ,GLORIA; ROMERO,EDUARDO; HERNÁNDEZ-TAMAMES,JUAN ANTONIO; MOLINA,VICENTE; MALPICA,NORBERTO. This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples. Tipo: Info:eu-repo/semantics/article Palavras-chave: Neurodegenerative disease; Structural MRI; Pattern classification; SPM; VBM; DARTEL; Support vector machines. Ano: 2010 URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-548X2010000300012 Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network Anais da ABC (AABC) MELO,CLÊNIA A.O. DE; LOPES,JULIANE G.; ANDRADE,ALEXSANDRA O.; TRINDADE,ROQUE M.P.; MAGALHÃES,ROBSON S.. Abstract: Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the... Tipo: Info:eu-repo/semantics/article Palavras-chave: Arbuscular mycorrhizal fungi spores; Artificial neural network; Circle Hough transform; Image preprocessing; Pattern classification; Semi-automated counting. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700901 Registros recuperados: 2 Primeira ... 1 ... Última

Empresa Brasileira de Pesquisa Agropecuária - Embrapa Todos os direitos reservados, conforme Lei n° 9.610 Política de Privacidade Área restrita		Embrapa Parque Estação Biológica - PqEB s/n° Brasília, DF - Brasil - CEP 70770-901 Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC: https://www.embrapa.br/fale-conosco