doi: 10.3934/dcdss.2019089

X-ray image global enhancement algorithm in medical image classification

1. 

School of Computer Science, Sichuan University of Science & Engineering, Zigong, China

2. 

School of Film and Television, Sichuan Vocational College of Cultural Industries, Chengdu, China

3. 

Dept. of Mathematics and Statistics, Winona State University, Winona, MN 55987, USA

* Corresponding author: Wenzhong Zhu

Received  June 2017 Revised  December 2017 Published  November 2018

The current global enhancement algorithm for medical X-ray image has problems of poor de-noising and enhancement effect and low reduction of the enhanced medical X-ray image. To address the problems, a global enhancement algorithm for X-ray image in medical image classification is proposed in this paper. The medical X-ray image is gray scaled, which provides the basis for the further processing of the image. The noise in medical X-ray image is removed by using multi-wavelet transform to improve the enhancement effect of the method. With the curve-let domain the medical X-ray image is enhanced, the reduction degree of medical X-ray image is improved and the global enhancement of the medical X-ray image is completed. Experimental results show that the de-noising effect of the proposed method is effective, the enhanced medical X ray image is better, and the reduction degree of medical X-ray image is high.

Citation: Wenzhong Zhu, Huanlong Jiang, Erli Wang, Yani Hou, Lidong Xian, Joyati Debnath. X-ray image global enhancement algorithm in medical image classification. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2019089
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Y. Jiang, J. Zhai and F. Department, Details enhancement algorithm of fuzzy image based on wavelet packet layered purification, Bulletin of Science & Technology, 96-98.

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Z. K., Y. J., C. J. and et al, Phase extraction algorithm considering high-order harmonics in fringe image processing, Applied Optics, 4989.

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N. Kamiyama, Ultrasonic diagnosis apparatus and medical image processing method, Journal of the Acoustical Society of America, 28 (2015), 1088.

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H. KhalilD. KimY. Jo and K. Park, Optical derotator alignment using image-processing algorithm for tracking laser vibrometer measurements of rotating objects, Review of Scientific Instruments, 88 (2017), 11510.

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Y. H. Li, Text feature selection algorithm based on chi-square rank correlation factorization, Journal of Interdisciplinary Mathematics, 20 (2017), 153-160.

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S. L. P., K. B. and A. M., Optimal transport for particle image velocimetry: Real data and postprocessing algorithms, Siam Journal on Applied Mathematics, 75 (2015), 2495-2514. doi: 10.1137/140988814.

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S. Neal, Image processing algorithm performance prediction on different hardware architectures, Nuclear Physics A, 444 (2015), 303-324.

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D. PapamichailE. PantelisP. PapagiannisP. Karaiskos and E. Georgiou, A web simulation of medical image reconstruction and processing as an educational tool., Journal of Digital Imaging, 28 (2015), 24-31.

[15]

A. ParchamiB. S. GildehS. M. TaheriM. MashinchiA. ParchamiB. S. GildehS. M. TaheriM. MashinchiA. Parchami and B. S. Gildeh, A general p-value-based approach for testing quality by considering fuzzy hypotheses, Journal of Intelligent & Fuzzy Systems, 32 (2017), 1649-1658.

[16]

W. Peng, Research on model of student engagement in online learning., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 2869-2882.

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X. QinH. WangY. DuH. Zheng and Z. Liang, Structured light image enhancement algorithm based on retinex in hsv color space, Journal of Computer-Aided Design & Computer Graphics, 25 (2013), 308-314.

[18]

O. R. and I. K., Ultrasonic diagnostic apparatus, medical image processing apparatus, Journal of the Acoustical Society of America, 1088.

[19]

D. SuiZ. Jiao and J. Yang, Image enhancement algorithm based on wavelet analysis and retinex algorithm, Journal of Jilin University, 54 (2016), 592-596.

[20]

L. Wang, Study on the method of super-resolution image little feature enhancement and simulation, Computer Simulation, 373-376.

[21]

Y. Wang, N. Motomura and Y. Wang, Medical image processing apparatus, medical image device and image processing method, 2014.

[22]

Z. W. M., D. W., L. H. and et al, Infrared image enhancement algorithm based on multisensor images, Journal of China Academy of Electronics and Information Technology, 32 (2017), 346-352.

[23]

R. YuanM. LuoZ. SunS. ShiP. Xiao and Q. Xie, Rayplus: A web-based platform for medical image processing, Journal of Digital Imaging, 30 (2017), 197-203.

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H. ZhangD. ZengH. ZhangJ. WangZ. Liang and J. Ma, Applications of nonlocal means algorithm in low-dose x-ray ct image processing and reconstruction: A review, Medical Physics, 44 (2017), 1168-1185.

show all references

References:
[1]

H. BiB. ZhangZ. Wang and W. Hong, L q regularisation-based synthetic aperture radar image feature enhancement via iterative thresholding algorithm, Electronics Letters, 52 (2016), 1336-1338.

[2]

S. Chandramohan and I. Avrutsky, Enhancing sensitivity of a miniature spectrometer using a real-time image processing algorithm, Applied Spectroscop, 70 (2016), 756.

[3]

S. ChenS. Kao and H. Su, On degree-sequence characterization and the extremal number of edges for various hamiltonian properties under fault tolerance, Discrete Mathematics and Theoretical Computer Science, 17 (2016), 307-314.

[4]

C. C. ConlinJ. L. ZhangF. RoussetC. VachetY. ZhaoK.A. MortonK. CarlstonG. Gerig and V. S. Lee, Performance of an efficient image-registration algorithm in processing mr renography data, Journal of Magnetic Resonance Imaging, 43 (2016), 391-397.

[5]

N. H., P. V., T. T. and et al, Smartphone and mobile image processing for assisted living: Health-monitoring apps powered by advanced mobile imaging algorithms, IEEE Signal Processing Magazine, 52 (2016), 30-48.

[6]

L. M. Jawad and G. Sulong, Chaotic map-embedded blowfish algorithm for security enhancement of colour image encryption, Nonlinear Dynamics, 81 (2015), 2079-2093. doi: 10.1007/s11071-015-2127-9.

[7]

Y. Jiang, J. Zhai and F. Department, Details enhancement algorithm of fuzzy image based on wavelet packet layered purification, Bulletin of Science & Technology, 96-98.

[8]

Z. K., Y. J., C. J. and et al, Phase extraction algorithm considering high-order harmonics in fringe image processing, Applied Optics, 4989.

[9]

N. Kamiyama, Ultrasonic diagnosis apparatus and medical image processing method, Journal of the Acoustical Society of America, 28 (2015), 1088.

[10]

H. KhalilD. KimY. Jo and K. Park, Optical derotator alignment using image-processing algorithm for tracking laser vibrometer measurements of rotating objects, Review of Scientific Instruments, 88 (2017), 11510.

[11]

Y. H. Li, Text feature selection algorithm based on chi-square rank correlation factorization, Journal of Interdisciplinary Mathematics, 20 (2017), 153-160.

[12]

S. L. P., K. B. and A. M., Optimal transport for particle image velocimetry: Real data and postprocessing algorithms, Siam Journal on Applied Mathematics, 75 (2015), 2495-2514. doi: 10.1137/140988814.

[13]

S. Neal, Image processing algorithm performance prediction on different hardware architectures, Nuclear Physics A, 444 (2015), 303-324.

[14]

D. PapamichailE. PantelisP. PapagiannisP. Karaiskos and E. Georgiou, A web simulation of medical image reconstruction and processing as an educational tool., Journal of Digital Imaging, 28 (2015), 24-31.

[15]

A. ParchamiB. S. GildehS. M. TaheriM. MashinchiA. ParchamiB. S. GildehS. M. TaheriM. MashinchiA. Parchami and B. S. Gildeh, A general p-value-based approach for testing quality by considering fuzzy hypotheses, Journal of Intelligent & Fuzzy Systems, 32 (2017), 1649-1658.

[16]

W. Peng, Research on model of student engagement in online learning., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 2869-2882.

[17]

X. QinH. WangY. DuH. Zheng and Z. Liang, Structured light image enhancement algorithm based on retinex in hsv color space, Journal of Computer-Aided Design & Computer Graphics, 25 (2013), 308-314.

[18]

O. R. and I. K., Ultrasonic diagnostic apparatus, medical image processing apparatus, Journal of the Acoustical Society of America, 1088.

[19]

D. SuiZ. Jiao and J. Yang, Image enhancement algorithm based on wavelet analysis and retinex algorithm, Journal of Jilin University, 54 (2016), 592-596.

[20]

L. Wang, Study on the method of super-resolution image little feature enhancement and simulation, Computer Simulation, 373-376.

[21]

Y. Wang, N. Motomura and Y. Wang, Medical image processing apparatus, medical image device and image processing method, 2014.

[22]

Z. W. M., D. W., L. H. and et al, Infrared image enhancement algorithm based on multisensor images, Journal of China Academy of Electronics and Information Technology, 32 (2017), 346-352.

[23]

R. YuanM. LuoZ. SunS. ShiP. Xiao and Q. Xie, Rayplus: A web-based platform for medical image processing, Journal of Digital Imaging, 30 (2017), 197-203.

[24]

H. ZhangD. ZengH. ZhangJ. WangZ. Liang and J. Ma, Applications of nonlocal means algorithm in low-dose x-ray ct image processing and reconstruction: A review, Medical Physics, 44 (2017), 1168-1185.

Figure 1.  Gray contour line of image
Figure 2.  System structure of multi-wavelet decomposition and reconstruction
Figure 3.  Denoising results of three methods
Figure 4.  PSNR values of three methods
Figure 5.  Degree of reduction of three methods
Table 1.  Test results of three methods
Number of iterations PSNR/dB MSE/dp
The proposed method Retinex-based method Double plateaus histogram-based method The proposed method Retinex-based method Double plateaus histogram-based method
1 18.9672 13.2654 11.6587 824.839 965.325 978.547
2 18.9658 13.6548 12.3689 823.657 942.354 968.348
3 19.5781 12.6849 11.3589 836.348 951.347 946.256
4 19.6875 13.6528 10.3647 846.268 912.487 925.645
5 18.6597 11.3549 12.0367 851.267 937.985 971.648
6 20.3698 12.4872 9.2657 865.215 978.654 985.157
7 21.8571 11.8627 9.5489 836.259 996.125 977.627
8 24.6257 10.6894 12.3647 841.025 984.367 955.348
9 23.1459 10.8547 10.3658 823.024 971.254 957.518
10 22.6587 9.3657 9.6581 856.237 956.185 975.264
Number of iterations PSNR/dB MSE/dp
The proposed method Retinex-based method Double plateaus histogram-based method The proposed method Retinex-based method Double plateaus histogram-based method
1 18.9672 13.2654 11.6587 824.839 965.325 978.547
2 18.9658 13.6548 12.3689 823.657 942.354 968.348
3 19.5781 12.6849 11.3589 836.348 951.347 946.256
4 19.6875 13.6528 10.3647 846.268 912.487 925.645
5 18.6597 11.3549 12.0367 851.267 937.985 971.648
6 20.3698 12.4872 9.2657 865.215 978.654 985.157
7 21.8571 11.8627 9.5489 836.259 996.125 977.627
8 24.6257 10.6894 12.3647 841.025 984.367 955.348
9 23.1459 10.8547 10.3658 823.024 971.254 957.518
10 22.6587 9.3657 9.6581 856.237 956.185 975.264
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