Incorporating Breast Asymmetry Studies into CADx Systems

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

Contralateral asymmetry for breast cancer detection : A CADx approach

Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subjectwise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog- and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breas