• Laser & Optoelectronics Progress
  • Vol. 56, Issue 3, 031006 (2019)
Xiangdan Hou1、2, Mengjing Zheng1、2, Hongpu Liu1、2、*, and Bocen Li1、2
Author Affiliations
  • 1 School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
  • 2 Hebei Provincial Key Laboratory of Big Data Computing, Tianjin 300401, China
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    DOI: 10.3788/LOP56.031006 Cite this Article Set citation alerts
    Xiangdan Hou, Mengjing Zheng, Hongpu Liu, Bocen Li. Medical Image Enhancement Algorithm Based on Shearlet Domain and Improve Pal-King Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031006 Copy Citation Text show less
    Shearlet transformation in frequency domain
    Fig. 1. Shearlet transformation in frequency domain
    Shearlet transform of frequency domain support space
    Fig. 2. Shearlet transform of frequency domain support space
    (a) Membership function curve of the original image; (b)improved membership function curve
    Fig. 3. (a) Membership function curve of the original image; (b)improved membership function curve
    Experimental results of different algorithms for lung image. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    Fig. 4. Experimental results of different algorithms for lung image. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    Experimental results for different methods. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    Fig. 5. Experimental results for different methods. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    Experimental results for different methods. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    Fig. 6. Experimental results for different methods. (a) Original image; (b) shearlet[10] method; (c) fractional differential[16] method; (d) improved Pal-King[14] method; (e) proposed method
    AlgorithmEvaluation indicator
    AGEINTCSTDE
    Shearlet10.4393.9516.3284.526.80
    Fractional differential7.5566.3412.1649.136.51
    Improved Pal-King10.91104.4915.78108.034.92
    Proposed algorithm13.27121.6720.13107.144.98
    Table 1. Comparison of average objective criteria for lung images
    AlgorithmEvaluation indicator
    AGEINTCSTDE
    Shearlet4.8649.085.6444.105.41
    Fractional differential4.8846.196.2926.065.28
    Improved Pal-King6.1362.637.0649.653.67
    Proposed algorithm9.5477.168.6460.523.72
    Table 2. Comparison of objective criteria for different algorithms in Fig. 5
    AlgorithmEvaluation indicator
    AGEINTCSTDE
    Shearlet4.7049.025.3042.696.09
    Fractional differential4.9649.606.0228.065.94
    Improved Pal-King5.0754.095.5847.293.95
    Proposed algorithm6.4768.457.1854.574.00
    Table 3. Comparison of objective criteria for different algorithms in Fig. 6
    Xiangdan Hou, Mengjing Zheng, Hongpu Liu, Bocen Li. Medical Image Enhancement Algorithm Based on Shearlet Domain and Improve Pal-King Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031006
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