Multi-Individual Mammographic Image Registration Based on Global-Local Integrated Transformations

Mammography has been the most reliable and effective screening tool for the early detection of breast cancer. Registration of mammographic images plays an important role in computer-aided detection (CAD) systems for detecting breast cancer in mammograms. For the purpose of comparison based abnormality detection., mammographic image registration is usually performed on temporal mammograms or right and left mammograms of one object. We propose a method for multi-individual mammographic image registration in order to analyze mammograms of different women under the same framework. This enables normality or abnormality modelling based on different objects. However., the traditional free-form model based registration algorithm cannot register mammographic images of different individuals accurately., and the registration efficiency is low. In this paper., a non-rigid image registration method based on an integration of global coarse registration and local fine registration is presented. The mean squared gray-level difference is used as the similarity measure. The Limited-memory Broyden Fletcher Goldfarb Shanno (L- BFGS) optimization algorithm is employed to optimize the registration parameters. Ten pairs of mammographic images of different cases selected from the Mammographic Image Analysis Society (MIAS) database are used for experiments. Experimental results show that., compared with the traditional free-form model based algorithm and the Diffeomorphic Demons registration algorithm., it is indicated that the proposed method improves the registration accuracy and efficiency of multi-individual mammographic images.