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International Journal of Biomedical Imaging
Volume 2016, Article ID 7987212, 13 pages
Research Article

Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images

1Department of Electrical and Computer Engineering, University of São Paulo, 400 Avenida Trabalhador São-Carlense, 13566-590 São Carlos, SP, Brazil
2Department of Physics, University of São Paulo, 3900 Avenida Bandeirantes, 14040-901 Ribeirão Preto, SP, Brazil

Received 1 March 2016; Accepted 17 May 2016

Academic Editor: Sonal Jain

Copyright © 2016 Karem D. Marcomini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This research presents a methodology for the automatic detection and characterization of breast sonographic findings. We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material. When the results were considerable, we applied the same techniques to clinical examinations. The process was started employing preprocessing (Wiener filter, equalization, and median filter) to minimize noise. Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling us to consider the neural network SOM as the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP) classifier. The accuracy achieved during training with simulated images was 94.2%, producing an AUC of 0.92. To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators and to clinical trials, resulting in an accuracy of 90% and 81%, respectively. The proposed classifier proved to be an important tool for the diagnosis in breast ultrasound.