Citation
Quintanilla Domínguez, Joel and Ojeda Magaña, Benjamín and Marcano Cedeño, Alexis Enrique and Cortina Januchs, María Guadalupe and Andina de la Fuente, Diego
(2011).
Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks.
"EURASIP journal on advances in signal processing"
(n. 91);
pp. 1-11.
ISSN 1687-6172.
https://doi.org/10.1186/1687-6180-2011-91.
Abstract
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
