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Doctoral Thesis
DOI
10.11606/T.3.2013.tde-23052014-010946
Document
Author
Full name
Jamilson Bispo dos Santos
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2013
Supervisor
Committee
Almeida Junior, Jorge Rady de (President)
Araújo, Sidnei Alves de
Furuie, Sérgio Shiguemi
Medeiros, Regina Bitelli
Sato, Liria Matsumoto
Title in Portuguese
Pesquisa de similaridades em imagens mamográficas com base na extração de características.
Keywords in Portuguese
CBIR
Data mining
Processamento digital de imagens
Reconhecimento de padrões
Abstract in Portuguese
Este trabalho apresenta uma estratégia computacional para a consolidação do treinamento dos radiologistas residentes por meio da classificação de imagens mamográficas pela similaridade, analisando informações dos laudos realizados por médicos experientes, obtendo os atributos extraídos das imagens médicas. Para a descoberta de padrões que caracterizam a similaridade aplicam-se técnicas de processamento digital de imagens e de mineração de dados nas imagens mamográficas. O reconhecimento de padrões tem como objetivo realizar a classificação de determinados conjuntos de imagens em classes. A classificação dos achados mamográficos é realizada utilizando Redes Neurais Artificiais, por meio do classificador Self-Organizing Map (SOM). O presente trabalho utiliza a recuperação de imagens por conteúdo (CBIR- Content-Based Image Retrieval), considerando a similaridade em relação a uma imagem previamente selecionada para o treinamento. As imagens são classificadas de acordo com a similaridade, analisando-se informações dos atributos extraídos das imagens e dos laudos. A identificação da similaridade é obtida pela extração de características, com a utilização da transformada de wavelets.
Title in English
Search for similarities in mammographic images based feature extraction.
Keywords in English
CBIR
Data mining
Digital image processing
Pattern recognition
Abstract in English
This work presents a computational strategy to consolidate the training of residents radiologists through the classification of mammographic images by similarity, analyzing information from reports made by experienced physicians, obtaining the attributes extracted from medical images. For the discovery of patterns that characterize the similarity apply techniques of digital image processing and data mining in mammographic images. Pattern recognition aims to achieve the classification of certain sets of images in classes. The classification of mammographic is performed using Artificial Neural Networks, through the classifier Self-Organizing Map (SOM). This work uses the image retrieval (CBIR-Content- Based Image Retrieval), considering the similarity in relation to an image already selected for training. The images are classified according to similarity, analyzing attribute information extracted from the images and reports. The identification of similarity was obtained by feature extraction, using the technique of wavelet transform.
 
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Publishing Date
2014-05-28
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • Santos, J. B., ALMEIDA JR, J. R., and SILVA, L. A. Pattern Recognition im Mammographic Images Used by the Residents in Mammography. In ICCMA 2013 - Internatinal Conference on Computer Medical Applications, Sousse, 2013. Proceedings of Internatinal Conference on Computer Medical Applications., 2013.
All rights of the thesis/dissertation are from the authors
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