Towards CNN-Based Registration of Craniocaudal and Mediolateral Oblique 2-D X-ray Mammographic Images

Abstract

We investigate methodologies for the automated registration of pairs of 2-D X-ray mammographic images, taken from the two standard mammographic angles. We present two exploratory techniques, based on Convolutional Neural Networks, to examine their potential for co-registration of findings on the two standard mammographic views. To test algorithm performance, our analysis uses a synthetic, surrogate data set for performing controlled experiments, as well as real 2-D X-ray mammogram imagery. The preliminary results are promising, and provide insights into how the proposed techniques may support multi-view X-ray mammography image registration currently and as technology evolves in the future.