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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 |
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.
Mathematics Subject Classification:
60B12.
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,
2019, 12
(4&5)
: 1297-1309.
doi: 10.3934/dcdss.2019089
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O. R. and I. K., Ultrasonic diagnostic apparatus, medical image processing apparatus, Journal of the Acoustical Society of America, 1088. Google Scholar |
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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. Google Scholar |
| [20] |
L. Wang, Study on the method of super-resolution image little feature enhancement and simulation, Computer Simulation, 373-376. Google Scholar |
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Y. Wang, N. Motomura and Y. Wang, Medical image processing apparatus, medical image device and image processing method, 2014. Google Scholar |
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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. Google Scholar |
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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. Google Scholar |
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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. Google Scholar |
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. Google Scholar |
| [2] |
S. Chandramohan and I. Avrutsky, Enhancing sensitivity of a miniature spectrometer using a real-time image processing algorithm, Applied Spectroscop, 70 (2016), 756. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
| [8] |
Z. K., Y. J., C. J. and et al, Phase extraction algorithm considering high-order harmonics in fringe image processing, Applied Optics, 4989. Google Scholar |
| [9] |
N. Kamiyama, Ultrasonic diagnosis apparatus and medical image processing method, Journal of the Acoustical Society of America, 28 (2015), 1088. Google Scholar |
| [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. Google Scholar |
| [11] |
Y. H. Li, Text feature selection algorithm based on chi-square rank correlation factorization, Journal of Interdisciplinary Mathematics, 20 (2017), 153-160. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
| [16] |
W. Peng, Research on model of student engagement in online learning., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 2869-2882. Google Scholar |
| [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. Google Scholar |
| [18] |
O. R. and I. K., Ultrasonic diagnostic apparatus, medical image processing apparatus, Journal of the Acoustical Society of America, 1088. Google Scholar |
| [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. Google Scholar |
| [20] |
L. Wang, Study on the method of super-resolution image little feature enhancement and simulation, Computer Simulation, 373-376. Google Scholar |
| [21] |
Y. Wang, N. Motomura and Y. Wang, Medical image processing apparatus, medical image device and image processing method, 2014. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
| [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. Google Scholar |
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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