Método de detecção de massas em mamas densas usando análise de componentes independentes

• Ano de defesa: 2017
• Autor(a) principal: SILVA, Luis Claudio de Oliveira
• Orientador(a): BARROS FILHO, Allan Kardec Duailibe
• Banca de defesa: SANTANA, Ewaldo Eder Carvalho , FONSECA NETO, João Viana da , RIBEIRO, Aurea Celeste da Costa , OLIVEIRA, Fausto Lucena de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Análise de Imagem Médica; Mamas Densas; Filtragem; Agrupamento; Segmentação de Imagens
• Palavras-chave em Inglês: Medical Image Analysis; Dense Breast; Filtering; Clustering; Image Segmentation
• Área do conhecimento CNPq: Processamento Gráfico; Cancerologia
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1897

Breast cancer is the second type of cancer that most a ects women in the world, losing only for non melanoma skin cancer. Breast density can hinder the location of masses, especially in early stages. In this work, the use of independent component analysis for detecting and segmentation lesions in dense breasts is proposed. Several works suggests the use of computer aided diagnosis, increasing sensitivity to over 90% in detecting cancer in non dense breasts, however there are few published studies about detecting in dense breasts. To analyse its e ciency in relation to other segmentation techniques, we compare the performance with principal component analysis. To measure the quality of the segmentation obtained by the two methods, a area overlay measure will be used. To verify if there was any di erence between the results of the proposed methods in the detection of lesions in nondense breasts and in dense breasts, a statistic test for two proportions was used. Experimental results on the Mini-MIAS and DDSM database showed an accuracy of 92.71% in detecting masses in nondense and 79.17% in dense breasts. All experiments showed that the ICA lters have a better performance for detect lesions in dense breast, compared with PCA. Contrary to previous works, our experiments showed that there is actually a signi cant di erence between the detection of masses in dense and nondense breasts. This study can help specialist to detect lesions in dense breast.