Descripción
|
|
---|---|
A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection. | |
Internacional
|
Si |
JCR del ISI
|
Si |
Título de la revista
|
EURASIP journal on advances in signal processing |
ISSN
|
1687-6172 |
Factor de impacto JCR
|
1,01 |
Información de impacto
|
|
Volumen
|
|
DOI
|
10.1186/1687-6180-2011-91 |
Número de revista
|
|
Desde la página
|
1 |
Hasta la página
|
11 |
Mes
|
SIN MES |
Ranking
|