Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

• Published in: EURASIP journal on advances in signal processing 2011-10, Vol.2011 (1), p.1-11, Article 91
• Main Authors: Quintanilla-Domínguez, Joel, Ojeda-Magaña, Benjamín, Marcano-Cedeño, Alexis, Cortina-Januchs, María G, Vega-Corona, Antonio, Andina, Diego
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Classifiers; Engineering; Fuzzy; Fuzzy logic; Image and Video Quality Improvement Techniques for Emerging Applications; Image detection; Mathematical analysis; Quantum Information Technology; Signal,Image and Speech Processing; Spintronics; Standard deviation
• ISSN: 1687-6180; 1687-6172; 1687-6180
• DOI: 10.1186/1687-6180-2011-91

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.