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.
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Notes
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.
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Available at http://www.cancerimagingarchive.net/
References
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
Baudes A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530
Cai JF, Candes EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982
Cai JF, Osher S (2013) Fast singular value thresholding without singular value decomposition. Methods Appl Anal 20(4):335–352
Candes EJ, Recht B (2009) Exact low-rank matrix completion via convex optimization. Found Comput Math 9(6):717–772
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
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
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
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
Guo Q, Zhang C, Liu Q, Zhang Y, Shen X (2014) Image interpolation based on nonlocal self-similarity. ScienceAsia 40(2):168–174
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
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
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
Hardie R (2007) A fast image super resolution algorithm using an adaptive wiener filter. IEEE Trans Image Process 16(12):2953–2964
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
Hossain MS (2016) Patient state recognition system for healthcare using speech and facial expression. J Med Syst 40(12):272:1–272:8
Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Comput Netw 101:192–202
Hossain MS, Muhammad G (2016) Healthcare big data voice pathology assessment framework. IEEE Access 4(1):7806–7815
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
Irani M, Peleg S (1993) Motion analysis for image enhancement: resolution, occlusion, and transparency. J Visual Commun Image Represent 4(4):324–335
Jafari-Khouzani K (2014) MRI upsampling using feature-based nonlocal means approach. IEEE Trans Med Imaging 33(10):1969–1985
Korman S, Avidan S (2011) Coherency sensitive hashing. In: Proceedings of IEEE International Conference on Computer Vision, pp 1607–1614
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
Larsen RM (1998) Lanczos bidiagonalization with partial reorthogonalization. DAIMI Rep Ser 537:1–101
Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049–1075
Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527
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
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
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
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
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
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
Manjon JV, Coupe P, Buades A, Fonov V, Collins DL (2010) Non-local MRI upsampling. Med Image Anal 14:784–792
Ning Q, Chen K, Yi L (2013) Image super-resolution via analysis sparse prior. IEEE Signal Process Lett 20(4):399–402
Olshansky SJ, Carnes BA, Yang YC et al. (2016) The future of smart health. Computer 49(11):14–21
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
Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: A technical overview. IEEE Signal Process Mag 20(3):21–36
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
Schaeffer H, Osher S (2013) A low patch-rank interpretation of texture. SIAM J Imaging Sci 6(1):226–262
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
Thevenaz P, Blu T, Unser M (2000) Interpolation revisited. IEEE Trans Med Imaging 19(7):739–758
Tomasi C, Manduchi R (1998) Bilateral fitlering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, pp 836–846
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
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
Yang J, Wright J, Huang TS, Ma Y (2010) Image super resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873
Yang MC, Wang YCF (2013) A self-learning approach to single image super-resolution. IEEE Trans Multimed 15(3):498–508
Yap PT, An H, Chen Y, Shen D (2014) Fiber-driven resolution enhancement of diffusion-weighted images. NeuroImage 84(1):939–950
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
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
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
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
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.
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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
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DOI: https://doi.org/10.1007/s11042-017-5277-6