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





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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