A A fast and fully automatic registration approach for mammographic images

  • Asiyeh golnari
  • Nasrin Taherkhani, Dr Payam Noor University
  • Mahboubeh Shamsi, Dr Qom University of Technology
  • Mohammad Nabi Rezaee Payam Noor University
Keywords: Mammogram registration, Image processing, Thin-Plate Spline, Mutual Information, change detection.

Abstract

Mammogram registration is one of the important steps in breast cancer progression assessment. In this paper we present an automated approach for mammographic images registration which is an improved method based on a previously proposed approach by the authors. The proposed method will run on a sample of 27 mammograms from the Parto Teb Azma Imaging Center. In the first step of this method, initial misalignment between fixed image and moving images are removed by using pre-processing of Mammograms. Then, we extract corresponding point pairs with using Local curvature and image intensity features as parameters for the TPS transformation function. At last, a pair of mammograms is registered using Thin-Plate spline interpolation based on corresponding points on the two images. Performance of proposed method is measured by correlation coefficient (CC) and Sum of squared differences (SSD) criterions. We show that in addition to maximizing similarities and minimizing differences between two images, the second image retains its overall shape. The average of correlation coefficient of mammograms after image registration in the automated method increase to 0.3253 and the average of difference squared in the automated method will decrease to 0.1055.

References

1. Hipwell JH, Vavourakis V, Han L, Mertzanidou T, Eiben B, Hawkes DJJPiM, et al. A review of biomechanically informed breast image registration. 2016;61(2):R1.
2. Golnari A, Shamsi M, Rasouli A, Rezaee MN. Semi-automatic Registration of Mammographic Images for Breast Cancer Progression Assessment. Majlesi Journal of Multimedia Processing. 2015;3(4).
3. Mani V. Survey of medical image registration. Journal of Biomedical Engineering and Technology. 2013;1(2):8-25.
4. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE transactions on medical imaging. 1999;18(8):712-21.
5. Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Physics in Medicine and Biology. 2009;54(7):1849.
6. Zhong Y-j, Chen L-Z, editors. A Novel Algorithm Based on SIFT and Graph Transformation for Mammogram Registration. Proceedings of the 2012 International Conference on Cybernetics and Informatics; 2014: Springer.
7. Kierkels R, den Otter L, Korevaar E, Langendijk J, van der Schaaf A, Knopf A, et al. An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images. 2018;63(4):045026.
8. Ramani R, Vanitha NS, Valarmathy S. The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images. International Journal of Image, Graphics & Signal Processing. 2013;5(4).
9. Gonzalez RC, Woods RE. Steven L. Eddins Digital Image Processing. 3 rd ed: Prentice Hall; 2008.
10. Yang J, Williams JP, Sun Y, Blum RS, Xu C. A robust hybrid method for nonrigid image registration. Pattern Recognition. 2011;44(4):764-76.
11. Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision: Cengage Learning; 2014.
12. Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Physics in medicine and biology. 2001;46(3):R1.
13. Shannon CE. Communication in the presence of noise. Proceedings of the IEEE. 1984;72(9):1192-201.
14. Liu Y, Cheng H, Huang J, Zhang Y, Tang X, Tian J. An Effective Non-rigid Registration Approach for Ultrasound Image Based On “Demons” Algorithm. Journal of digital imaging. 2013;26(3):521-9.
15. Chiou Y-C, Lin C-Y, Liang Y-T, editors. A New Method for the Registration of Mammograms. 9th Joint International Conference on Information Sciences (JCIS-06); 2006: Atlantis Press.
Published
2019-04-17
Issue
Vol 6 No 2 (2017): June
Section
Articles
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