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Title: Incorporating Breast Asymmetry Studies into CADx Systems
Authors: Celaya Padilla, José María
Guzmán Valdivia, César Humberto
Galván Tejada, Jorge Issac
Galván Tejada, Carlos Eric
Gamboa Rosales, Hamurabi
Delgado Contreras, Juan Rubén
Martinez Torteya, Antonio
Olivera Reyna, Roberto
Manjarrez Sánchez, Jorge Roberto
Martínez Ruíz, Francisco Javier
Garza Veloz, Idalia
Martínez Fierro, Margarita de la Luz
Traviño, Victor
Tamez Peña, José Gerardo
Issue Date: 4-Oct-2017
Publisher: IntechOpen
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.
ISBN: 978-953-51-3558-6
Other Identifiers: info:eu-repo/semantics/publishedVersion
Appears in Collections:*Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada

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