Temporal mammogram image registration using optimized curvilinear coordinates

Registration of mammograms plays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalities. The comparison of mammograms requires a registration between them. A temporal mammogram registration method is proposed in this paper. It is based on the curvilinear coordinates, which are utilized to cope both with global and local deformations in the breast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance between manually defined landmarks is decreased. In addition, a thorough comparison with the state-of-the-art mammogram registration methods is performed to show its effectiveness.

[1]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[2]  Nico Karssemeijer,et al.  An Anatomically Oriented Breast Coordinate System for Mammogram Analysis , 2011, IEEE Transactions on Medical Imaging.

[3]  J. Modersitzki,et al.  A unified approach to fast image registration and a new curvature based registration technique , 2004 .

[4]  Leo Grady,et al.  Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations , 2013, International Journal of Computer Vision.

[5]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[6]  Nico Karssemeijer,et al.  A comparison of methods for mammogram registration , 2003, IEEE Transactions on Medical Imaging.

[7]  Samir Kumar Bandyopadhyay,et al.  Accurate Breast Contour Detection Algorithms in Digital Mammogram , 2011 .

[8]  Kevin W. Bowyer,et al.  Registration and difference analysis of corresponding mammogram images , 1999, Medical Image Anal..

[9]  K. Mariasb,et al.  Registration and matching of temporal mammograms for detecting abnormalities , 2006 .

[10]  Christine Tanner,et al.  Automated registration of diagnostic to prediagnostic x-ray mammograms: Evaluation and comparison to radiologists' accuracy. , 2010, Medical physics.

[11]  Michael Brady,et al.  A registration framework for the comparison of mammogram sequences , 2005, IEEE Transactions on Medical Imaging.

[12]  Vennila Ramalingam,et al.  Automated assessment of breast tissue density in digital mammograms , 2010, Comput. Vis. Image Underst..

[13]  Yrjö Neuvo,et al.  Detail-preserving median based filters in image processing , 1994, Pattern Recognit. Lett..

[14]  Vijay Rajagopal,et al.  Creating individual-specific biomechanical models of the breast for medical image analysis. , 2008, Academic radiology.

[15]  Laurent D. Cohen,et al.  A new Image Registration technique with free boundary constraints: application to mammography , 2003, Comput. Vis. Image Underst..

[16]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[17]  Michael Brady,et al.  Multi-scale landmark selection for improved registration of temporal mammograms , 2000 .

[18]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Domenec Puig,et al.  A Novel Mammography Image Representation Framework with Application to Image Registration , 2014, 2014 22nd International Conference on Pattern Recognition.

[20]  R. Chandrasekhar,et al.  Gross segmentation of mammograms using a polynomial model , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Nicole Vincent,et al.  Visual Perception Driven Registration of Mammograms , 2010, 2010 20th International Conference on Pattern Recognition.

[22]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[23]  Nikos Paragios,et al.  DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. , 2011, Medical image analysis.

[24]  Nikos Paragios,et al.  DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting , 2009, IPMI.

[25]  Michael Brady,et al.  Curvilinear Structure Based Mammographic Registration , 2005, CVBIA.

[26]  Radhika Sivaramakrishna,et al.  Breast image registration techniques: a survey , 2006, Medical and Biological Engineering and Computing.

[27]  Byung-Woo Hong,et al.  Structural Comparison of Mammograms , 2005, BMVC.

[28]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.

[29]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  Xavier Lladó,et al.  Revisiting Intensity-Based Image Registration Applied to Mammography , 2011, IEEE Transactions on Information Technology in Biomedicine.

[31]  João Manuel R S Tavares,et al.  Medical image registration: a review , 2014, Computer methods in biomechanics and biomedical engineering.

[32]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[33]  Arnau Oliver,et al.  A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..