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Lossless Compression Algorithm for Medical Images With High Precision Based on Discrete Wavelet Transform

Published: 29 June 2022 Publication History

Abstract

In image distortion and low adaptive recognition after medical image compression, a high precision medical image lossless compression algorithm based on discrete wavelet transform is proposed. A 3D imaging model of multi-dimensional medical images is constructed, and adaptive information enhancement and image restoration processing are performed on the collected medical images. According to the results of high-dimensional segmentation and segmentation, discrete wavelet transform is used to achieve high-precision lossless compression of medical images. The results show that the medical image compression is better non-destructive and the image fidelity is higher, which improves the detection and adaptive recognition ability of medical images

References

[1]
Amri, H., Khalfallah, A., Gargouri, M., Nebhani, N., Lapayre, J.-C., & Bouhlel, M.-S. (2017). Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression. Journal of Signal Processing Systems for Signal, Image, and Video Technology, 87(2), 203–214.
[2]
Bai, F., & Cao, Z. R. (2018). Design of white light-thermal imaging dual-channel image recognition system based on deep learning. Kexue Jishu Yu Gongcheng, 18(21), 269–272.
[3]
Chen, J., Guan, B., Wang, H., Zhang, X., Tang, Y., & Hu, W. (2018). Image Thresholding Segmentation Based on Two-Dimensional Histogram Using Gray Level and Local Entropy Information. IEEE Access: Practical Innovations, Open Solutions, 6(99), 5269–5275.
[4]
Chen, N., & Wu, J. B. (2019). Image encryption algorithm in discrete orthogonal S-transform domain based on discrete wavelet transform. Optics Technology, 045(003), 348–354.
[5]
Chen, Y. X., Huang, Z. C., & Feng, L. (2017). Image compression algorithm based on singular value decomposition and Contourlet transform. Jisuanji Yingyong Yanjiu, 34(1), 317–320.
[6]
Deng, H., Yin, D. H., & Liu, B. T. (2017). Medical image compression method based on geometric stream multilevel tree Bandelet segmentation coding. Jisuanji Yingyong Yanjiu, 34(11), 305–308.
[7]
Du, Y., & Zhao, H. (2017). Compression method of MWC sampling data based on principal component analysis. Jisuanji Yingyong, 34(3), 940–944.
[8]
Huang, W., & Wen, K. M. (2017). Research on 4K UHD image sharpening technology based on improved neural network. Modern Electronic Technology, 40(14), 120–123.
[9]
Khadidos, A., Sanchez, V., & Li, C. T. (2017). Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation. IEEE Transactions on Image Processing, 26(4), 1979–1991. 28186897.
[10]
Li, X., Wang, X., & Dai, Y. (2017). Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation. Journal of Signal Processing Systems for Signal, Image, and Video Technology, 90(2), 1–17.
[11]
Liu, X., Peng, X., Liu, L., Wu, G., Zhao, C., Wang, F., & Cai, Y. (2017). Self-reconstruction of the degree of coherence of a partially coherent vortex beam obstructed by an opaque obstacle. Applied Physics Letters, 110(18), 810–296.
[12]
Mu, L., & Cheng, L. L. (2017). Adaptive learning model based on k-anonymous location privacy protection. Computer Engineering and Applications, 53(18), 89–94.
[13]
Shang, M., Xin, L., & Liu, Z. (2018). Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications. IEEE/CAA Journal of Automatica Sinica, 6(1), 134-144.
[14]
Song, H., Sun, Y., Wang, R., Zhang, B., Li, N., Wang, Y., & Fei, W. (2017). Statistically homogeneous pixel selection for small SAR data sets based on the similarity test of the covariance matrix. Remote Sensing Letters, 8(10), 927–936.
[15]
Song, T. Q., & Xing, Z. H. (2017). A method for generating light-relief model based on gray-scale compression. Computer Engineering, 43(10), 253–258.
[16]
Wang, X., Gao, J. M., & Liang, Y. (2018). An efficient storage multi-level 2D 9 / 7 discrete wavelet transform structure. Journal of Xi’an Jiaotong University, 52(04), 116–121.
[17]
Xue, S., Wang, G. X., & Guo, J. Z. (2017). A layered compression method for high-precision vector map encryption. Journal of Surveying and Mapping Science and Technology, 34(5), 535–540.
[18]
Yang, M. Z., Zou, Z., & Han, C. P. (2018). Segmental lossless compression of interferograms based on vector quantization and linear prediction. Infrared Technology, 40(7), 21–25.
[19]
Yelampalli, P. K. R., Nayak, J., & Gaidhane, V. H. (2018). Daubechies wavelet-based local feature descriptor for multimodal medical image registration. IET Image Processing, 12(10), 1692–1702.
[20]
Zhang, R., Shang, H., Yu, S., He, Q., Yuan, Y., Bolatov, K., & Mambetov, B. T. (2017). Tree‐ring‐based precipitation reconstruction in southern Kazakhstan, reveals drought variability. International Journal of Climatology, 37(2), 741–750.

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Published In

cover image International Journal of Grid and High Performance Computing
International Journal of Grid and High Performance Computing  Volume 14, Issue 1
Jun 2022
522 pages
ISSN:1938-0259
EISSN:1938-0267
Issue’s Table of Contents

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IGI Global

United States

Publication History

Published: 29 June 2022

Author Tags

  1. Discrete Wavelet Transform
  2. High Precision
  3. Lossless Compression
  4. Medical Image

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