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Morphological Changes of Collagen Fibers in Myocardium of Rats under Different Exercise Loads Based on Three-Dimensional Simulation Technique

  • Mobile & Wireless Health
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Abstract

In order to improve the visual analysis ability of the morphological changes of rat liulimyocardial collagen fibers under different exercise loads, a method of extracting the morphological changes of collagen fibers in rat myocardium under different exercise loads based on three-dimensional simulation technique is proposed. The three-dimensional morphological characteristics of the collagen fibers in the original rat myocardium are made by CT scanning technique. Like information collection, a gradient decomposition method is used to filter the three-dimensional morphological features of rat myocardial collagen fibers. The edge contour features of the three-dimensional morphological features of rat myocardial collagen fibers under different motion loads are extracted. The threshold segmentation method is used to carry out the rat myocardial glue under different exercise loads. The segmentation of the regional pixel feature block of the three-dimensional morphological features of the original fiber is segmented into a block vector with high resolution, and the regional reconstruction of the three-dimensional morphological features of the rat myocardial collagen fibers under different motion loads is carried out to realize the high resolution identification and classification of the 3D morphological features of the rat myocardial collagen fibers. The simulation results show that the three-dimensional simulation of the morphological changes of rat myocardial collagen fibers under different exercise loads is better, and the accuracy of feature extraction is higher.

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References

  1. Amor, B. B., Su, J., and Srivastava, A., Action recognition using rate-invariant analysis, of skeletal shape trajectories [J]. IEEE Trans. Patt. Anal. Mach. Intel. 38(1):1–13, 2016.

    Article  Google Scholar 

  2. Tan, Q. Y., Leung, H., Song, Y. et al., Multipath ghost suppression for through-the-wall-radar [J]. IEEE Trans. Aerosp. Electron. Syst. 50(3):2284–2292, 2014.

    Article  Google Scholar 

  3. Gennarelli, G., and Soldovieri, F., Multipath ghosts in radar imaging: Physical insight and mitigation strategies [J]. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 8(3):1078–1086, 2014.

    Google Scholar 

  4. Li, S., Jia, Y., Guo, Y., Zhong, X., and Cui, G., Moving target tracking algorithm based on improved Camshift for through-wall-radar imaging. J. Comput. Appl. 38(2):528–532, 2018.

    Google Scholar 

  5. Ferrara, P., and Bianchi, T., Image forgery localization via fine-grained analysis of CFA artifacts [J]. IEEE Trans. Inform. Forens. Sec. 7(5):1566–1577, 2012.

    Article  Google Scholar 

  6. Tengfei, L., and Weili, J., Automatic line segment registration using Gaussian mixture model and expectation-maximization algorithm [J]. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 7(5):1688–1699, 2014.

    Article  Google Scholar 

  7. Siwei, L., Xunyu, P., and Xing, Z., Exposing region splicing forgeries with blind local noise estimation [J]. Int. J. Comput. Vis. 110(2):202–221, 2014.

    Article  Google Scholar 

  8. Li, X., Ge, B., Luo, Q., Li, Y., and Tian, Q., Acquisition of camera dynamic extrinsic parameters in free binocular stereo vision system. J. Comput. Appl. 37(10):2888–2894, 2017.

    Google Scholar 

  9. Zhang, Q., Cai, F., and Li, Z., Human action recognition based on coupled multi-hidden Markov model and depth image data. J. Comput. Appl. 38(2):454–457, 2018.

    Google Scholar 

  10. Shen, X. X., Zhang, H., and Gao, Z., Behavior recognition algorithm based on Kinect and pyramid feature [J]. J. Optoelect. Laser 2014(2):357–363.

  11. Tian, G. H., Yin, J. Q., Han, X. et al., A new method of human behavior recognition based on joint information [J]. Robot 36(3):285–292, 2014.

    Google Scholar 

  12. Moghaddam, Z., and Piccardi, M., Training initialization of hidden markov models in human action recognition [J]. IEEE Trans. Aut. Sci. Eng. 11(2):394–408, 2014.

    Article  Google Scholar 

  13. Xiu, C., and Ba, F., Target tracking based on the improved Camshift method [C]//CCDC 2016:Proceedings of the 2016 Chinese control and decision conference. Piscataway, NJ. IEEE:3600–3604, 2016.

  14. Zhang, L., and Qiao, T. Z., An binary segmentation algorithm for infrared image [J]. Infrared Technol. 36(8):649–651, 2014.

    Google Scholar 

  15. Gennarelli, G., and Soldovieri, F., Multipath ghosts in radar imaging:Physical insight and mitigation strategies [J]. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 8(3):1078–1086, 2014.

    Google Scholar 

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Jian, L. Morphological Changes of Collagen Fibers in Myocardium of Rats under Different Exercise Loads Based on Three-Dimensional Simulation Technique. J Med Syst 43, 154 (2019). https://doi.org/10.1007/s10916-019-1290-9

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