Outlier Modeling in Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new image registration technique with free boundary constraints: application to mammography
Computer Vision and Image Understanding - Special issue on nonrigid image registration
A New Approach For The Registration of Images With Inconsistent Differences
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Comparison of Multiple View Strategies to Reduce False Positives in Breast Imaging
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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In this paper, we present a new method for simultaneously registering mammograms and detecting abnormalities. We assume that pixels can be divided into two classes: normal tissue and abnormalities (lesions). We define the registration constraints as a mixture of two distributions which describe statistically image gray-level variations for both pixel classes. The two distributions are weighted at each pixel by the probability of abnormality presence. Using the Maximum A Posteriori, we estimate the registration transformation and the probability map of abnormality presence at the same time. We illustrate the properties of our technique with some experiments and compare it with some classical methods.