Detecting Mammographic Abnormalities from Image Registration Results

Authors:
Robert Martí;David Raba;Caroline Rubin;Reyer Zwiggelaar
Affiliations:
Computer Vision and Robotics Group, University of Girona, Spain;Computer Vision and Robotics Group, University of Girona, Spain;Breast Screening Unit, Royal South Hants Hospital, Southampton, UK;Department of Computer Science, University of Wales, Aberystwyth, UK
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 2
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Alignment by maximization of mutual information

Alignment by maximization of mutual information
Establishing the correspondence between control points in pairs of mammographic images

IEEE Transactions on Image Processing

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Abstract

One of the applications of image registration is to assess object differences from various images that have been spatially correlated. This paper discusses the use of features extracted from subtracted registered images in a classification framework with an aim to detect abnormal mammograms. Both quantitative and qualitative results are provided, which show that although non-optimal classification is obtained, region features extracted after registration can be used to discriminate between normal and abnormal mammograms.