Skip to main content
SpringerLink
Log in

Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Medical images have high information redundancy, which can be used to improve image analysis and visualization for purpose of healthcare. In order to recover a high-resolution (HR) image from its low-resolution (LR) counterpart, this paper proposes a resolution enhancement method by using the nonlocal self-similar redundancy and the low-rank prior. The proposed method consists of three main steps. First, an initial HR image is generated by nonlocal interpolation, which is based on the self-similarity of medical images. Next, the low-rank minimum variance estimator is exploited to reconstruct the HR image. At last, we iteratively apply the subsampling consistency constraint and perform the low-rank reconstruction to refine the reconstructed HR result. Experimental results conducted on MR and CT images demonstrate that the proposed method outperforms conventional interpolation methods and is competitive with the current stat-of-the-art methods in terms of both quantitative metrics and visual quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Though Euclidean distance is incapable of efficiently capturing the intrinsic similarity between image patches, one advantage of this metric is its simplicity of computation. Thus we use it to measure the similarity of patches.

  2. Available at http://brainweb.bic.mni.mcgill.ca/

  3. Available at http://www.cancerimagingarchive.net/

References

  1. Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans Graphics 28(3):Article 24

    Article  Google Scholar 

  2. Baudes A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530

    Article  MathSciNet  MATH  Google Scholar 

  3. Cai JF, Candes EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982

    Article  MathSciNet  MATH  Google Scholar 

  4. Cai JF, Osher S (2013) Fast singular value thresholding without singular value decomposition. Methods Appl Anal 20(4):335–352

    MathSciNet  MATH  Google Scholar 

  5. Candes EJ, Recht B (2009) Exact low-rank matrix completion via convex optimization. Found Comput Math 9(6):717–772

    Article  MathSciNet  MATH  Google Scholar 

  6. Cao F, Cai M, Tan Y (2015) Image interpolation via low-rank matrix completion and recovery. IEEE Trans Circ Syst Video Technol 25(8):1261–1270

    Article  Google Scholar 

  7. Collins DL, Zijdenbos AP, Kollokian V et al (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17(3):463–468

    Article  Google Scholar 

  8. Dong W, Zhang L, Shi G, Wu X (2009) Nonlocal back-projection for adaptive image enlargement. In: Proceeding of IEEE International Conference on Image Processing, pp 349–352

  9. Dong W, Zhang L, Lukac R, Shi G (2013) Sparse representation based image interpolation with non-local autoregressive modeling. IEEE Trans Image Process 22(4):1382–1394

    Article  MathSciNet  MATH  Google Scholar 

  10. Guo Q, Zhang C, Liu Q, Zhang Y, Shen X (2014) Image interpolation based on nonlocal self-similarity. ScienceAsia 40(2):168–174

    Article  Google Scholar 

  11. Guo Q, Zhang C, Zhang Y, Liu H, Shen X (2015) Low-rank image denoising based on minimum variance estimator. J Comput-Aided Des Comput Graph 27(12):2237–2246. In Chinese

    Google Scholar 

  12. Guo Q, Zhang C, Zhang Y, Liu H (2016) An efficient SVD-based method for image denoising. IEEE Trans Circ Syst Video Technol 26(5):868–880

    Article  Google Scholar 

  13. Guo Q, Gao S, Zhang X, Yin Y, Zhang C (2017) Patch-based image inpainting via two-stage low rank approximation. IEEE Trans Visualization and Computer Graphics, accepted

  14. Hardie R (2007) A fast image super resolution algorithm using an adaptive wiener filter. IEEE Trans Image Process 16(12):2953–2964

    Article  MathSciNet  Google Scholar 

  15. He K, Sun J (2012) Computing nearest-neighbor fields via propagation-assisted kd-trees. In: Proceedings of IEEE International Conference on Computer Vision, pp 111–118

  16. Hossain MS (2016) Patient state recognition system for healthcare using speech and facial expression. J Med Syst 40(12):272:1–272:8

    Article  Google Scholar 

  17. Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  18. Hossain MS, Muhammad G (2016) Healthcare big data voice pathology assessment framework. IEEE Access 4(1):7806–7815

    Article  Google Scholar 

  19. Hung KK, Siu Wc (2012) Single image super-resolution using iterative Wiener filter. In: Proceedings of IEEE International Conference on Acoustics Speech, Signal Processing, pp 1269–1272

  20. Irani M, Peleg S (1993) Motion analysis for image enhancement: resolution, occlusion, and transparency. J Visual Commun Image Represent 4(4):324–335

    Article  Google Scholar 

  21. Jafari-Khouzani K (2014) MRI upsampling using feature-based nonlocal means approach. IEEE Trans Med Imaging 33(10):1969–1985

    Article  Google Scholar 

  22. Korman S, Avidan S (2011) Coherency sensitive hashing. In: Proceedings of IEEE International Conference on Computer Vision, pp 1607–1614

  23. Kwan RKS, Evans AC, Pike GB (1999) MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging 18(11):1085–1097

    Article  Google Scholar 

  24. Larsen RM (1998) Lanczos bidiagonalization with partial reorthogonalization. DAIMI Rep Ser 537:1–101

    Google Scholar 

  25. Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049–1075

    Article  Google Scholar 

  26. Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527

    Article  Google Scholar 

  27. Li J, Huang XY, Li JW, Chen XF, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25(8):2201–2210

    Article  Google Scholar 

  28. Li J, Chen XF, Li MQ, Li JW, Lee P, Lou WJ (2014) Secure deduplication with efficient and reliable convergent key management. IEEE Trans Parallel Distrib Syst 25(6):1615–1625

    Article  Google Scholar 

  29. Li P, Li J, Huang Z, Li T, Gao CZ, Yiu SM, Chen K (2017) Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.02.006

    Article  Google Scholar 

  30. Li P, Li J, Huang Z, Gao CZ, Chen WB, Chen K (2017) Privacy-preserving outsourced classification in cloud computing. Cluster Computing, https://doi.org/10.1007/s10586-017-0849-9

    Article  Google Scholar 

  31. Liu H, Geng F, Guo Q, Zhang C, Zhang C (2017) A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm. Soft Computer, accepted

  32. Manjon JV, Coupe P, Buades A, Collins DL, Robles M (2010) MRI superresolution using self-similarity and image priors. Int J Biomed Imaging 2010:425891

    Article  Google Scholar 

  33. Manjon JV, Coupe P, Buades A, Fonov V, Collins DL (2010) Non-local MRI upsampling. Med Image Anal 14:784–792

    Article  Google Scholar 

  34. Ning Q, Chen K, Yi L (2013) Image super-resolution via analysis sparse prior. IEEE Signal Process Lett 20(4):399–402

    Article  Google Scholar 

  35. Olshansky SJ, Carnes BA, Yang YC et al. (2016) The future of smart health. Computer 49(11):14–21

    Article  Google Scholar 

  36. Pan Z, Yu J, Huang H, Hu S (2013) Super-resolution based on compressive sensing and structural self-similarity for remote sensing image. IEEE Trans Geosci Remote Sens 51(9):4864–4876

    Article  Google Scholar 

  37. Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: A technical overview. IEEE Signal Process Mag 20(3):21–36

    Article  Google Scholar 

  38. Ren C, He X, Teng Q, Wu Y, Nguyen TQ (2016) Single image super-resolution using local geometric duality and non-local similarity. IEEE Trans Image Process 25(5):2168–2183

    Article  MathSciNet  MATH  Google Scholar 

  39. Schaeffer H, Osher S (2013) A low patch-rank interpretation of texture. SIAM J Imaging Sci 6(1):226–262

    Article  MathSciNet  MATH  Google Scholar 

  40. Shi F, Cheng J, Wang L, Yap PT, Shen D (2015) LRTV: MR image super-resolution with low-rank and total variation regularizations. IEEE Trans Med Imaging 34(12):2459–2466

    Article  Google Scholar 

  41. Thevenaz P, Blu T, Unser M (2000) Interpolation revisited. IEEE Trans Med Imaging 19(7):739–758

    Article  Google Scholar 

  42. Tomasi C, Manduchi R (1998) Bilateral fitlering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, pp 836–846

  43. Trinh DH, Luong M, Dibos F, Rocchisani JM, Pham CD, Nguyen TQ (2014) Novel example-based method for super-resolution and denoising of medical images. IEEE Trsns Image Process 23(4):1882–1895

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  45. Yang J, Wright J, Huang TS, Ma Y (2010) Image super resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  46. Yang MC, Wang YCF (2013) A self-learning approach to single image super-resolution. IEEE Trans Multimed 15(3):498–508

    Article  Google Scholar 

  47. Yap PT, An H, Chen Y, Shen D (2014) Fiber-driven resolution enhancement of diffusion-weighted images. NeuroImage 84(1):939–950

    Article  Google Scholar 

  48. Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238

    Article  Google Scholar 

  49. Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21 (11):4544–4556

    Article  MathSciNet  MATH  Google Scholar 

  50. Zhang Y, Wu G, Yap PT, Feng Q, Liu J, Chen W, Shen D (2012) Hierarchical patch-based sparse representation-A new approach for resolution enhancement of 4D-CT lung data. IEEE Trans Med Imaging 31(11):1993–2005

    Article  Google Scholar 

  51. Zhang Y, Yap PT, Wu G, Feng Q, Liu J, Chen W, Shen D (2013) Resolution enhancement of lung 4D-CT data using multiscale interphase iterative nonlocal means. Med Phys 40(5):051916

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by National Natural Science Foundation (61572286, 61332015, and 61472220), Shandong Provincial Key Research and Development Plan (2017CXGC1504), Natural Science Foundation of Shandong Province (2016ZRB01143), and Fostering Project of Dominant Discipline an Talent Team of Shandong Province Higher Education. The authors also gratefully acknowledge the helpful comments and suggestions of the anonymous reviewers, which have improved the presentation significantly.

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Shandong University of Finance and Economics, Shandong, China

    Hui Liu & Qiang Guo

  2. Shandong Provincial Key Laboratory of Digital Media Technology, Shandong, China

    Hui Liu, Qiang Guo & Caiming Zhang

  3. Shandong Provincial Qianfoshan Hospital, Shandong, China

    Guangli Wang

  4. Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra, India

    B. B. Gupta

  5. Department of Computer Science and Technology, Shandong University, Shandong, China

    Caiming Zhang

Authors
  1. Hui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. B. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Caiming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Guo, Q., Wang, G. et al. Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior. Multimed Tools Appl 78, 9033–9050 (2019). https://doi.org/10.1007/s11042-017-5277-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5277-6

Keywords

Search

Navigation