Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.
J Digit Imaging
; 31(5): 680-691, 2018 10.
Article
em En
| MEDLINE
| ID: mdl-29582242
ABSTRACT
In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps:
pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Músculos Peitorais
/
Algoritmos
/
Neoplasias da Mama
/
Mamografia
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Interpretação de Imagem Radiográfica Assistida por Computador
/
Erros de Diagnóstico
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article