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基于塊稀疏的電阻抗成像算法

王琦,  張鵬程,  汪劍鳴,  李秀艷,  連志杰,  陳慶良,  陳彤云,  陳曉靜,  賀靜,  段曉杰,  王化祥

王琦, 張鵬程, 汪劍鳴, 李秀艷, 連志杰, 陳慶良, 陳彤云, 陳曉靜, 賀靜, 段曉杰, 王化祥. 基于塊稀疏的電阻抗成像算法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
引用本文: 王琦, 張鵬程, 汪劍鳴, 李秀艷, 連志杰, 陳慶良, 陳彤云, 陳曉靜, 賀靜, 段曉杰, 王化祥. 基于塊稀疏的電阻抗成像算法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
Citation: WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

基于塊稀疏的電阻抗成像算法

doi: 10.11999/JEIT170425
  • 2.

    (天津市胸科醫(yī)院　天津　300000)

  • 3.

    (天津大學(xué)電氣與自動(dòng)化工程學(xué)院 天津 300072)

基金項(xiàng)目: 

國家科技支撐計(jì)劃重點(diǎn)項(xiàng)目(2013BAF06B00)，國家自然科學(xué)基金(61601324, 61373104, 61402330, 61405143)，天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計(jì)劃(15JCQNJC01500)

Block-Sparse Reconstruction for Electrical Impedance Tomography

  • 2.

    (Tianjin Chest Hospital, Tianjin 300000, China)

  • 3.

    (School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China)

Funds: 

The Key Projects of National Science and Technology Support Program (2013BAF06B00), The National Natural Science Foundation of China (61601324, 61373104, 61402330, 61405143), The Natural Science Foundation of Tianjin Municipal Science and Technology Commission (15JCQNJC01500)

  • 摘要: 該文提出一種基于自適應(yīng)塊稀疏字典學(xué)習(xí)的電阻抗圖像重建算法，構(gòu)建了分塊稀疏字典，較好地保留了重建圖像的細(xì)節(jié)信息；同時(shí)，將字典學(xué)習(xí)與圖像重建交替進(jìn)行，并將迭代重建的中間結(jié)果作為稀疏字典的訓(xùn)練樣本，有效提高了字典學(xué)習(xí)效果。數(shù)值仿真與實(shí)驗(yàn)重建結(jié)果表明，新方法對電阻抗成像系統(tǒng)測量噪聲具有較好的魯棒性，能準(zhǔn)確重構(gòu)電導(dǎo)率分布圖像，特別是對突變細(xì)節(jié)的準(zhǔn)確恢復(fù)。
    Abstract: An electrical impedance image reconstruction algorithm based on adaptive block-sparse dictionary is proposed. A block-sparse dictionary is constructed creatively, which preferably preserves the details of reconstructed images. Meanwhile, the sparsifying dictionary optimization and image reconstruction are performed alternately, and the intermediate result of the iterative reconstruction is used as the training sample of the sparse dictionary, which can effectively improve the learning effect of the dictionary. The numerical simulation and experiment results show that the patch-based sparsity method for measure noise has excellent robustness and can accurately reconstruct the conductivity distribution image, especially the precise details of mutation.
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  • WANG Q, LIAN Z, WANG J, et al. Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation[J]. Review of Scientific Instruments, 2016, 87(11): 114707, doi: 10.1063/1.4966998.
    SBARBARO D, VAUHKONEN M, and JOHANSEN T A. State estimation and inverse problems in electrical impedance tomography: observability, convergence and regularization[J]. Inverse Problems, 2015, 31(4): 045004, doi: 10.1088/0266- 5611/31/4/045004.
    Ye J, WANG H, and YANG W. Image reconstruction for electrical capacitance tomography based on sparse representation[J]. IEEE Transactions on Instrumentation Measurement, 2015, 64(1): 89-102. doi: 10.1109/TIM.2014. 2329738.
    LIU Y, YANG Z, and YANG L. Online signature verification based on DCT and sparse representation[J]. IEEE Transactions on Cybern, 2015, 45(11): 2498-2511. doi: 10.1109/TCYB.2014.2375959.
    NAZZAL M and OZKARAMANLI H. Wavelet domain dictionary learning-based single image superresolution[J]. Signal, Image and Video Processing, 2015, 1(7): 1-11. doi: 10.1007/s11760-013-0602-7.
    WIECZOREK M, FRIKEL J, VOGEL J, et al. X-ray computed tomography using curvelet sparse regularization[J]. Medical Physics, 2015, 42(4): 1555-1567. doi: 10.1118/ 1.4914368.
    LIU Y, LIU S, and WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164. doi: 10.1016/j.inffus.2014.09.004.
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    WANG Q, WANG H, ZHANG R, et al. Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography[J]. Review of Scientific Instruments, 2012, 83(10): 104707. doi: 10.1063/1.4760253.
    YUE B, WANG S, LIANG X, et al. Robust coupled dictionary learning with 1-norm coefficients transition constraint for noisy image super-resolution[J]. Signal Processing, 2017, 140: 177-189. doi: 10.1016/j.sigpro.2017. 04.015.
    QU X, HOU Y, LAM F, et al. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator[J]. Medical Image Analysis, 2014, 18(6): 843-856. do: 10.1016/j.media.2013.09.007.
    HEMMING B, FAGERLUND A, and LASSILA A. Linearized solution to electrical impedance tomography based on the Schur conjugate gradient method[J]. Measurement Science Technology, 2007, 18(11): 3373-3383. doi: 10.1088/0957-0233/18/11/017.
    WANG M. Inverse solutions for electrical impedance tomography based on conjugate gradients methods[J]. Measurement Science Technology, 2001, 13(1): 101-117. doi: 10.1088/0957-0233/13/1/314.
    BAO C, CAI J F, and JI H. Fast sparsity-based orthogonal dictionary learning for image restoration[C]. IEEE International Conference on Computer Vision IEEE, Sydney, 2014: 3384-3391. doi: 10.1109/ICCV.2013.420.
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出版歷程
  • 收稿日期:  2017-05-09
  • 修回日期:  2017-12-15
  • 刊出日期:  2018-03-19

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