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Journal of Medical Systems

, 40:184 | Cite as

A New Method for Computed Tomography Angiography (CTA) Imaging via Wavelet Decomposition-Dependented Edge Matching Interpolation

  • Zeyu Li
  • Yimin Chen
  • Yan Zhao
  • Lifeng Zhu
  • Shengqing Lv
  • Jiahui Lu
Education & Training
Part of the following topical collections:
  1. Education & Training

Abstract

The interpolation technique of computed tomography angiography (CTA) image provides the ability for 3D reconstruction, as well as reduces the detect cost and the amount of radiation. However, most of the image interpolation algorithms cannot take the automation and accuracy into account. This study provides a new edge matching interpolation algorithm based on wavelet decomposition of CTA. It includes mark, scale and calculation (MSC). Combining the real clinical image data, this study mainly introduces how to search for proportional factor and use the root mean square operator to find a mean value. Furthermore, we re- synthesize the high frequency and low frequency parts of the processed image by wavelet inverse operation, and get the final interpolation image. MSC can make up for the shortage of the conventional Computed Tomography (CT) and Magnetic Resonance Imaging(MRI) examination. The radiation absorption and the time to check through the proposed synthesized image were significantly reduced. In clinical application, it can help doctor to find hidden lesions in time. Simultaneously, the patients get less economic burden as well as less radiation exposure absorbed.

Keywords

Wavelet transform Edge detection Matching point interpolation Root mean square 

Notes

Acknowledgments

This work was also supported by Research on the interative oversized screen modern film display technique (n.13-a303-15-w23). This work was also supported by Integration Demonstration of key digital medical technologies (No.2012AA02A612). National High Technology Research and Development Program (863 program).

References

  1. 1.
    Li, D., Mao, S., Flores, F., et al., Dual standard reference values of left ventricular volumetric parameters by cardiac CT angiography. J. Am. Coll. Cardiol. 59(13):E1362, 2012.CrossRefGoogle Scholar
  2. 2.
    Grevera, G.J., and Udupa, J.K., Shape-based interpolation of multidimensional grey-level images. IEEE Trans. Med. Imaging. 15(6):881–892, 1996.CrossRefPubMedGoogle Scholar
  3. 3.
    Grevera, G.J., and Udupa, J.K., An objective comparison of 3-D image interpolation methods. IEEE Trans. Med. Imaging. 17(4):642–652, 1998.CrossRefPubMedGoogle Scholar
  4. 4.
    Grevera, G.J., and Udupa, J.K., A task-specific evaluation of three-dimensional image interpolation techniques. IEEE Trans. Med. Imaging. 18(2):137–143, 1999.CrossRefPubMedGoogle Scholar
  5. 5.
    Chen, J., and Ma, W., A novel adaptive 3D medical image interpolation method based on shape. Int. Conf. Graph. Image Process. 8768:876823–876821, 2013.Google Scholar
  6. 6.
    Yue, Z., and Jiaxin, C., Medical image interpolation algorithm based on correlation. Comput. Meas. Control. 22(9):2918–2921, 2014.Google Scholar
  7. 7.
    Osareh, A., and Shadgar, B., A segmentation method of lung cavities using region aided geometric snakes. J. Med. Syst. 34(34):419–433, 2010.CrossRefPubMedGoogle Scholar
  8. 8.
    Yoon, S.W., Lee, C., Jin, K.K., et al., Wavelet-based multi-resolution deformation for medical endoscopic image segmentation. J. Med. Syst. 32(3):207–214, 2008.CrossRefPubMedGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., and Terzopoulos, D., Snakes: active contour models. Int. J. Comput. Vis. 1(4):321–331, 1988.CrossRefGoogle Scholar
  10. 10.
    Zheng, Y., Li, G., and Sun, X.H., A new external force for snakes based on the IGGVF[C]// image and signal processing. CISP '08. Congress IEEE. 2008:615–619, 2008.Google Scholar
  11. 11.
    Wang, X., and Fu, W., Optimized SIFT image matching algorithm[C]// automation and logistics. ICAL 2008. IEEE Int. Conf. IEEE. 2008:843–847, 2008.Google Scholar
  12. 12.
    Daliri, M.R., Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images. J. Med. Syst. 36(2):995–1000, 2011.CrossRefPubMedGoogle Scholar
  13. 13.
    Mantos, P.L.K., and Maglogiannis, I., Sensitive patient data hiding using a ROI reversible steganography scheme for DICOM images. J. Med. Syst. 40(6):1–17, 2016.CrossRefGoogle Scholar
  14. 14.
    Haleem, M.S., Han, L., van Hemert, J., et al., Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (SLO) images. J. Med. Syst. 40(6):1–19, 2016.CrossRefGoogle Scholar
  15. 15.
    Morlet, J., Arens, G., Forgeau, I., et al., Wave propagation and sampling theory. Geophysics. 47(2):222–236, 1982.CrossRefGoogle Scholar
  16. 16.
    Daubechies, B. I., Orthogonal basesofcompactly supported wavelets[C]// Comm. Pure Appl. Math. 2010.Google Scholar
  17. 17.
    McInerney, T., and Terzopoulos, D., Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2):99–108, 1996.CrossRefGoogle Scholar
  18. 18.
    Lepik, U., Numerical solution of evolution equations by the Haar wavelet method. Appl. Math. Comput. 185(1):695–704, 2007.Google Scholar
  19. 19.
    Lai, Y.K., and Kuo, C.C.J., A Haar wavelet approach to compressed image quality measurement. J. Vis. Commun. Image Represent. 11(1):17–40, 2000.CrossRefGoogle Scholar
  20. 20.
    Shen, T., and Zhang, Z.G., Edge detection of microcirculation images using snake model. J. Phys. Chem. B. 105(47):11849–11853, 2002.Google Scholar
  21. 21.
    Zhicheng, F. U., Xiaoqiang, L. I., University S, et al., Tongue image segmentation based on snake model and radial edge detection. J. Image Graph. 2009.Google Scholar
  22. 22.
    Li, T., Yi, Z., Zhi, L., and Dongcheng, H., An overview on snakes models. Comput. Eng. 31(9):1–3, 2005.Google Scholar
  23. 23.
    Bresson, X., Esedoḡlu, S., Vandergheynst, P., et al., Fast global minimization of the active contour/snake model. J. Math. Imaging Vision. 28(2):151–167, 2007.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zeyu Li
    • 1
    • 2
  • Yimin Chen
    • 1
  • Yan Zhao
    • 2
  • Lifeng Zhu
    • 2
  • Shengqing Lv
    • 1
  • Jiahui Lu
    • 1
  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  2. 2.Computer Center Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

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