Advertisement

An adaptive enhancement method for breast X-ray images based on the nonsubsampled contourlet transform domain and whale optimization algorithm

  • Chang-Jiang ZhangEmail author
  • Huan-Huan Nie
Original Article
  • 10 Downloads

Abstract

We propose a new method for breast X-ray image adaptive enhancement that combines nonsubsampled contourlet transform (NSCT) with the whale optimization algorithm (WOA). First, the mammography X-ray image was processed by histogram equalization to ensure global image contrast. The processed image was then decomposed into three layers in the NSCT domain. Each layer was each decomposed into two, four, and eight directions. A median filter was used to remove noise in the first and second layers. Then, a special edge filter was adopted to enhance each sub-band image, and two parameters are involved. WOA is used to automatically search the optimal two parameters. Blind image quality index (BIQI) adaptive function was used as an objective function of WOA. Then, inverse NSCT was employed to reconstruct the processed image, generating the final adaptive enhancement image. The digital database for screening mammography (DDSM) was used to verify the performance of the proposed method. Five objective evaluation indexes, including information entropy, average gradient, standard deviation, contrast improvement index (CII), and BIQI, are combined together to construct a new comprehensive index to evaluate the visual quality of the enhanced image. The results show that the proposed method has a good enhancement effect for mammography X-ray images. The overall performance of the proposed method is better than some existing similar methods.

Graphical abstract

.

Keywords

Mammography X-ray image NSCT WOA BIQI Adaptive enhancement 

Notes

References

  1. 1.
    Chen W, Zheng R, Baade PD et al (2015) Cancer statistics in China [J]. CA Cancer J Clin, 2016 66(2):115–132.  https://doi.org/10.3322/caac.21338 CrossRefGoogle Scholar
  2. 2.
    Siegel RL, Miller KD, Jemal A (2015) Cancer statistics [J]. CA Cancer J Clin, 2010 60(5):277–300.  https://doi.org/10.3322/caac.20073 Google Scholar
  3. 3.
    Torre LA, Bray F, Siegel RL et al (2012) Global cancer statistics [J]. CA Cancer J Clin, 2015 65(2):87–108.  https://doi.org/10.3322/caac.21262 CrossRefGoogle Scholar
  4. 4.
    Li Y, Wang W, Yu D(1994) Application of adaptive histogram equalization to x-ray chest images [J]. Proceedings of SPIE-The International Society for Optical Engineering, 2321.  https://doi.org/10.1117/12.182056
  5. 5.
    Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations [J]. Comp Vision Graphics Image Proc 39(3):355–368.  https://doi.org/10.1016/S0734-189X(87)80186-X CrossRefGoogle Scholar
  6. 6.
    Shanmugavadivu P, Narayanan SGL (2014) Segmentation of microcalcification regions in digital mammograms using self-guided region-growing [C]. International Conference on Emerging Trends in Science.  https://doi.org/10.1109/INCOSET.2012.6513918
  7. 7.
    Chen H, Li A, Kaufman L et al (1994) A fast filtering algorithm for image enhancement [J]. IEEE Trans Med Imaging 13(3):557–564.  https://doi.org/10.1109/42.310887 CrossRefGoogle Scholar
  8. 8.
    Lee YH, Park SY (1990) A study of convex/concave edges and edge-enhancing operators based on the Laplacian [J]. IEEE Trans Circuits Syst 37(7):940–946.  https://doi.org/10.1109/31.55069 CrossRefGoogle Scholar
  9. 9.
    Panetta K, Zhou Y, Agaian SS et al (2011) Nonlinear Unsharp masking for mammogram enhancement [J]. IEEE Trans Inform Technol Biomed: a publication of the IEEE Engineering in Medicine and Biology Society 15(6):918–928.  https://doi.org/10.1109/TITB.2011.2164259 CrossRefGoogle Scholar
  10. 10.
    Laine AF, Schuler S, Fan J et al (1994) Mammographic feature enhancement by multiscale analysis [J]. IEEE Trans Med Imaging 13(4):725–740.  https://doi.org/10.1109/42.363095 CrossRefGoogle Scholar
  11. 11.
    Elsherif MS, Elsayad A (2001) Wavelet packet denoising for mammogram enhancement[M]. IEEE.  https://doi.org/10.1109/MWSCAS.2001.986144
  12. 12.
    Sakellaropoulos P, Costaridou L, Panayiotakis G (2003) A wavelet-based spatially adaptive method for mammographic contrast enhancement[J]. Phys Med Biol 48(6):787–803.  https://doi.org/10.1088/0031-9155/48/6/307 CrossRefGoogle Scholar
  13. 13.
    Papadopoulos A, Fotiadis D I, Costaridou L (2008) Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. [M]. Pergamon Press, Inc.  https://doi.org/10.1016/j.compbiomed.2008.07.006
  14. 14.
    Tang J, Liu X, Sun Q (2009) A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms[J]. IEEE J Select Topics Signal Proc 3(1):74–80.  https://doi.org/10.1109/jstsp.2008.2011108 CrossRefGoogle Scholar
  15. 15.
    Gou X, Liang Z (2014) A new breast image wavelet fusion method to enhance [J]. Comp Appl Softw 31(01):201–203Google Scholar
  16. 16.
    Mejia J, Ochoa H, Vergara O, Maynez LO (2009) The nonsubsampled Contourlet transform for enhancement of microcalcifications in digital mammograms[M]. MICAI 2009: advances in artificial intelligence.  https://doi.org/10.1007/978-3-642-05258-3_26
  17. 17.
    Doynov P, Tankasala SP (2016) Ultrafast blur evaluation in ocular biometrics[C]. TechnolHomeland Security.  https://doi.org/10.1109/THS.2016.7568934
  18. 18.
    Shuicai W, Peng Q, Gao HJ, Weiwei W. An adaptive enhancement method based on mammography [P]. Beijing: CN104616255A, 2015-05-13Google Scholar
  19. 19.
    Yousefi P (2015) Mammographic image enhancement for breast cancer detection applying wavelet transform[C]. 2015 IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES)Google Scholar
  20. 20.
    Pak F, Kanan HR, Alikhassi A (2015) Breast Cancer detection and classification in digital mammography based on non-subsampled Contourlet transform (NSCT) and super resolution[J]. Comput Methods Prog Biomed 122(2):89–107.  https://doi.org/10.1016/j.cmpb.2015.06.009
  21. 21.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm[J]. Adv Eng Softw 95:51–67.  https://doi.org/10.1016/j.advengsoft.2016.01.008
  22. 22.
    Hasanien HM (2018) Whale optimisation algorithm for automatic generation control of interconnected modern power systems including renewable energy sources[J]. IET Gener Transm Distrib 12(3):607–614.  https://doi.org/10.1049/iet-gtd.2017.1005
  23. 23.
    Sanad Ahmed A, Attia MA, Hamed NM, Abdelaziz AY. (2017) Comparison between genetic algorithm and whale optimization algorithm in fault location estimation in power systems[C]. 2017 Nineteenth International Middle East Power Systems Conference (MEPCON)Google Scholar
  24. 24.
    Shufang Z, Cong Z, Tao Z, et al (2015) Review on universal no-reference image quality assessment algorithm[J]. Comp Eng ApplGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.College of Physics and Electronic Information EngineeringZhejiang Normal UniversityJinhuaChina
  2. 2.College of Telecommunication Engineering, Zhejiang Post and Telecommunication CollegeShaoxingChina

Personalised recommendations