Automatic Registration of Mammograms Based on Linear Structures
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Local Orientation Distribution as a Function of Spatial Scale for Detection of Masses in Mammograms
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
A new image registration technique with free boundary constraints: application to mammography
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Application of Kohonen network for automatic point correspondence in 2D medical images
Computers in Biology and Medicine
Detecting Mammographic Abnormalities from Image Registration Results
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Non-rigid mammogram registration using demons algorithm: preliminary results
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Multimodal genetic algorithms-based algorithm for automatic point correspondence
Pattern Recognition
Breast density segmentation using texture
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Curvilinear structure based mammographic registration
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Level-line primitives for image registration with figures of merit
Integrated Computer-Aided Engineering
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This paper describes part of a study aimed at developing a computer-based aid for mammogram screening that makes a detailed comparison between mammograms of the same patient acquired at different screenings and detects changes indicative of cancer. The focus is on determining control points in two mammograms; these points are used to put two mammograms into correspondence. The paper details the algorithm for identifying the potential control points and establishing the correspondence between the two sets of control points. The algorithm's performance was evaluated by three observers, one of whom is an experienced radiologist, and found to be adequate