Group-wise mammographie image alignment based on entropy minimization

Breast cancer is especially a concern in woman and it is one of the most leading causes of death among woman in the world. The adoption of classical statistical modeling techniques has standardized computer vision in last few years. For better recognition information, most recognition algorithms depend on careful positioning of an object into a canonical pose, so that we can have meaningful information examined. Currently, a new statistical-theoretic approach is presented for finding the alignment (using entropy minimization with brightness transformation) of mammographic images of differing patients in this paper. Registration is achieved by adjusting the relative position and orientation until the sum of the pixel-stack entropies is minimized. Segmentation analysis of digital or digitized mammographic images was firstly carried out on mammographic images of left and right breast images for 161 patients, obtained from Mammographic Image Analysis Society (MIAS) database. Given a set of unaligned exemplars of a class, such as breast, we applied an automatically built alignment mechanism, no additional labeling of parts or poses in the dataset. Using this alignment mechanism, new members of the class, such as an unseen mammographic image from a new patient, can be precisely aligned for the recognition process. Our alignment method can improve the performance on a breast abnormality recognition task, both over unaligned images and over images aligned with the proposed alignment algorithm.