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Retraction: Xiaohong Zhu, Zili Yang and Tabharit Zoubir, Research on the matching algorithm for heterologous image after deformation in the same scene
August & September  2019, 12(4&5): 1297-1309. 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 and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1297-1309. doi: 10.3934/dcdss.2019089
##### References:
 [1] H. Bi, B. Zhang, Z. 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. Chen, S. 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. Conlin, J. L. Zhang, F. Rousset, C. Vachet, Y. Zhao, K.A. Morton, K. Carlston, G. 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. Khalil, D. Kim, Y. 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. Papamichail, E. Pantelis, P. Papagiannis, P. 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. Parchami, B. S. Gildeh, S. M. Taheri, M. Mashinchi, A. Parchami, B. S. Gildeh, S. M. Taheri, M. Mashinchi, A. 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. Qin, H. Wang, Y. Du, H. 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. Sui, Z. 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. Yuan, M. Luo, Z. Sun, S. Shi, P. Xiao and Q. Xie, Rayplus: A web-based platform for medical image processing, Journal of Digital Imaging, 30 (2017), 197-203. [24] H. Zhang, D. Zeng, H. Zhang, J. Wang, Z. 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. Bi, B. Zhang, Z. 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. Chen, S. 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. Conlin, J. L. Zhang, F. Rousset, C. Vachet, Y. Zhao, K.A. Morton, K. Carlston, G. 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. Khalil, D. Kim, Y. 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. Papamichail, E. Pantelis, P. Papagiannis, P. 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. Parchami, B. S. Gildeh, S. M. Taheri, M. Mashinchi, A. Parchami, B. S. Gildeh, S. M. Taheri, M. Mashinchi, A. 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. Qin, H. Wang, Y. Du, H. 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. Sui, Z. 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. Yuan, M. Luo, Z. Sun, S. Shi, P. Xiao and Q. Xie, Rayplus: A web-based platform for medical image processing, Journal of Digital Imaging, 30 (2017), 197-203. [24] H. Zhang, D. Zeng, H. Zhang, J. Wang, Z. 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.
Gray contour line of image
System structure of multi-wavelet decomposition and reconstruction
Denoising results of three methods
PSNR values of three methods
Degree of reduction of three methods
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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