**Incorporating Breast Asymmetry Studies into CADx Systems Systems**

**Incorporating Breast Asymmetry Studies into CADx** 

DOI: 10.5772/intechopen.69526

José María Celaya Padilla, Cesar Humberto Guzmán Valdivia, Jorge Issac Galván Tejada, Carlos Eric Galván Tejada, Hamurabi Gamboa Rosales, Juan Rubén Delgado Contreras, Antonio Martinez-Torteya, Roberto Olivera Reyna, Jorge Roberto Manjarrez Sánchez, Francisco Javier Martinez Ruiz, Idalia Garza-Veloz, Margarita L. Martinez-Fierro, Victor Treviño and Jose Gerardo Tamez-Peña Guzmán Valdivia, Jorge Issac Galván Tejada, Carlos Eric Galván Tejada, Hamurabi Gamboa Rosales, Juan Rubén Delgado Contreras, Antonio Martinez-Torteya, Roberto Olivera Reyna, Jorge Roberto Manjarrez Sánchez, Francisco Javier Martinez Ruiz, Idalia Garza-Veloz, Margarita L. Martinez-Fierro, Victor Treviño and Jose Gerardo Tamez-Peña Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

José María Celaya Padilla, Cesar Humberto

http://dx.doi.org/10.5772/intechopen.69526

#### **Abstract**

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects.

**Keywords:** breast cancer, asymmetry, bilateral registration, CAD

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
