X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach

  • Ching-Ta Lu
  • Mu-Yen Chen
  • Jun-Hong Shen
  • Ling-Ling Wang
  • Neil Y. Yen
  • Chia-Hua Liu
Original Research
  • 12 Downloads

Abstract

An X-ray bio-image might suffer interference from salt-and-pepper (SAP) noise during transmission or capture, thus reducing image quality. This paper proposes a three-stage method to cope with this problem. A directional-weighted-mean (DWM) filter is used to remove the corruption noise in the first stage. In the second stage, extreme pixel (255 or 0 for an 8-bit gray level bio-image) confirmation is performed to restore the X-ray bio-images. In the final stage, block matching identifies blocks with similar textures in a local region. The center pixels of these similar blocks are then averaged to refine the gray value of the restored pixel, thus allowing improvement to the quality of the restored X-ray image through consideration of the texture properties in neighbor pixels over a large size window. Experimental results show that the proposed approach can effectively remove background noise from a SAP noise corrupted bio-image for various noise densities. The reconstructed bio-image does not incur blurring even under heavy noise corruption.

Keywords

Directional-weighted-mean filter X-ray bio-image denoising Block matching Post-processing Salt-and-pepper noise 

Notes

Acknowledgements

The authors wish to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract Grants nos. MOST 105-2410-H-025-015-MY2, MOST 104-2221-E-468-007 and MOST 106-2410-H-468-009. The authors also gratefully acknowledges the Editor and anonymous reviewers for their valuable comments and constructive suggestions.

References

  1. Ahmed F, Das S (2014) Removal of high density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean. IEEE Trans Fuzzy Syst 22(5):1352–1358CrossRefGoogle Scholar
  2. Bhadauria HS, Dewal ML (2013) Medical image denoising using adaptive fusion of curvelet transform and total variation. Comput Electr Eng 39(5):1451–1460CrossRefGoogle Scholar
  3. Chen PY, Lien CY (2008) An efficient edge-preserving algorithm for removal of salt-and-pepper noise. IEEE Signal Process Lett 14:833–836CrossRefGoogle Scholar
  4. Deivalakshmi S, Palanisamy P (2016) Removal of high density salt and pepper noise through improved tolerance based selective arithmetic mean filtering with wavelet thresholding. Int J Electron Commun (AEU) 70(6):757–776CrossRefGoogle Scholar
  5. Deng X, Ma Y, Dong M (2016) A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN. Pattern Recog Lett 79:8–17CrossRefGoogle Scholar
  6. Dong YQ, Xu SF (2007) A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process Lett 14(3):31–34CrossRefGoogle Scholar
  7. Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH (2011) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process Lett 18(5):287–290CrossRefGoogle Scholar
  8. Hsieh MH, Cheng FC, Shie MC, Ruan SJ (2013) Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images. Eng Appl Artif Intell 26(4):1333–1338CrossRefGoogle Scholar
  9. Khan A, Waqas M, Ali MR, Altalhi A, Alshomrani S, Shim SO (2016) Image de-noising using noise ratio estimation, K-means clustering and non-local means-based estimator. Comput Electr Eng 54:370–381CrossRefGoogle Scholar
  10. Li Z, Liu G, Xu Y, Cheng Y (2014) Modified directional weighted filter for removal of salt and pepper noise. Pattern Recognit Lett 40:113–120CrossRefGoogle Scholar
  11. Li Z, Cheng Y, Tang K, Xu Y, Zhang D (2015) A salt and pepper noise filter based on local and global image information. Neurocomputing 159:172–185CrossRefGoogle Scholar
  12. Liang W, Tang M, Jing L, Sangaiah AK, Huang Y (2017). SIRSE: a secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Comput Electr Eng. . https://doi.org/10.1016/j.compeleceng.2017.05.001 Google Scholar
  13. Liao X, Yin J, Guo S, Li X, Sangaiah AK (2017) Medical JPEG image steganography based on preserving inter-block dependencies. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2017.08.020 Google Scholar
  14. Liu L, Chen CLP, Zhou Y, You X (2015) A new weighted mean filter with a two-phase detector for removing impulse noise. Inf Sci 315:1–16MathSciNetCrossRefGoogle Scholar
  15. Lu CT, Chou TC (2012) Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recognit Lett 2012 33(10):1287–1295CrossRefGoogle Scholar
  16. Lu CT, Chen YY, Wang LL, Chang CF (2016) Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recognit Lett 80(1):188–199CrossRefGoogle Scholar
  17. Lu CT, Shen JH, Wang LL, Hsu CC, Liu LL (2017) Impulse noise denoising using confidence measure with non-sequential process order for X-ray bio-images. J Med Biol Eng.  https://doi.org/10.1007/s40846-017-0356-8 Google Scholar
  18. Ravi Teja KV, Shanmukha Rao N, Santhosh Kumar P (2017) Distance based algorithm for removal of salt and pepper noise. In: Proceedings of the IEEE International conference on circuit, power and computing technologiesGoogle Scholar
  19. Sahin U, Uguz S, Sahin F (2014) Salt and pepper noise filtering with fuzzy-cellular automata. Comput Electr Eng 40(1):59–69CrossRefGoogle Scholar
  20. Samuel OW, Zhou H, Li X, Wang H, Zhang H, Sangaiah AK, Li G (2017a) Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2017.04.003 Google Scholar
  21. Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G (2017b) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172CrossRefGoogle Scholar
  22. Sree PSJ, Kumar P, Siddavatam R, Verma R (2013) Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets. Signal Image Video Process 7(1):111–118CrossRefGoogle Scholar
  23. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  24. Wang Y, Wang J, Song X, Han L (2016a) An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Signal Process Lett 23(11):1582–1586CrossRefGoogle Scholar
  25. Wang X, Shen S, Shi G, Xu Y, Zhang P (2016b) Iterative non-local means filter for salt and pepper noise removal. J Vis Commun Image Represent 38:440–450CrossRefGoogle Scholar
  26. Zhang R, Shen J, Wei F, Li X, Sangaiah AK (2017) Medical image classification based on multi-scale non-negative sparse coding. Artif Intell Med 83:44–51CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information CommunicationAsia UniversityTaichungTaiwan, ROC
  2. 2.Department of Medical Research, China Medical University HospitalChina Medical UniversityTaichungTaiwan, ROC
  3. 3.Department of Information ManagementNational Taichung University of Science and TechnologyTaichungTaiwan, ROC
  4. 4.School of Computer Science and EngineeringUniversity of AizuAizuwakamatsuJapan

Personalised recommendations