Journal of Innovative Optical Health Sciences, 2018, 11 (5): 1850028, Published Online: Dec. 26, 2018  

Contour reconstruction of three-dimensional spiral CT damage image

Author Affiliations
The First Affiliated Hospital of Jinzhou Medical University Jinzhou 121001, P. R. China
Abstract
In order to improve the diagnosis and analysis ability of 3D spiral CT and to reconstruct the contour of 3D spiral CT damage image, a contour reconstruction method based on sharpening template enhancement for 3D spiral CT damage image is proposed. This method uses the active contour LasSO model to extract the contour feature of the 3D spiral CT damage image and enhances the information by sharpening the template enhancement technique and makes the noise separation of the 3D spiral CT damage image. The spiral CT image was processed with ENT, and the statistical shape model of 3D spiral CT damage image was established. The gradient algorithm is used to decompose the feature to realize the analysis and reconstruction of the contour feature of the 3D spiral CT damage image, so as to improve the adaptive feature matching ability and the ability to locate the abnormal feature points. The simulation results show that in the 3D spiral CT damage image contour reconstruction, the proposed method performs well in the feature matching of the output pixels, shortens the contour reconstruction time by 20/ms, and provides a strong ability to express the image information. The normalized reconstruction error of CES is 30%, which improves the recognition ability of 3D spiral CT damage image, and increases the signal-to-noise ratio of peak output by 40 dB over other methods.
References

[1] S. K. Meher, "Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction," Eng. Appl. Artif. Intell. 30, 145–154 (2014).

[2] M. H. Hsieh, F. C. Cheng, M. C. Shie et al., "Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images," Eng. Appl. Artif. Intell. 26(4), 1333–1338 (2013).

[3] J. J. Wang, R. D. Wang, W. Li et al., "An information hiding algorithm for HEVC based on intra prediction," J. Optoelectron. Laser 25(8), 1578–1585 (2014).

[4] J. Zhang, "Binarization method with local threshold based on image blocks," J. Comp. Appl. 37(3), 827–831 (2017).

[5] J. F. Wang, Z. C. Huang, A. M. A. Talab, "New binarization method called BM aim to optimize detail of image," J. Wuhan Univ. Technol. 36(8), 127–132 (2014).

[6] Z. X. Lu, B. B. Zhang, "A new segmentation method of hand dorsal vein image," Microelectron. Comp. 31(8), 25–28 (2014).

[7] M. Zhang, W. Chen, L.-W. Chen et al., "Photorefractive long period waveguide grating filter in lithium niobate strip waveguide," Opt. Quant. Electron. 46, 1529–1538 (2014).

[8] J. Cao, H.-S. Li, Q. Cai, "Research on feature extraction of image target," Comp. Simul. 30(1), 409–413 (2013).

[9] W. Wang, Q. Yan, D. Jin, "Object-oriented remote sensing image classification based on GEPSO model," Comp. Sci. 42(5), 51–53 (2015).

[10] H.-C. Luo, Y.-Y. Li, J. Sun, "Filtering method for images based on adaptive neuro-fuzzy interference system," Comp. Sci. 40(7), 302–306 (2013).

[11] H. Yu, Z. Liu, Q. Tian, "A spectral prediction model of printer based on RBF neural network," Imaging Sci. Photochem. 33(3), 238–243 (2015).

[12] C. Liu, H. B. Zhao, C. S. Li et al., "CSP/SVMbased EEG classification of imagined hand movements," J. Northeast. Univ. (Natu. Sci.), 31(8), 1098–1101 (2010).

[13] F.Grimm,G.Naros,A.Gharabaghi,"Closed-loop task difficulty adaptation during virtual reality reach-tograsp training assisted with an exoskeleton for stroke rehabilitation," Front. Neurosci. 10, 518 (2016).

[14] M. Mukaino, T. Ono, K. Shindo et al., "Efficacy of brain–computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke," J. Rehabil. Med. 46(4), 378–382 (2014).

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

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

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

[18] Y. Huang, Z. Wu, L. Wang et al., "Feature coding in image classification: A comprehensive study," IEEE Trans. Patt. Anal. Mach. Intell. 36(3), 493–506 (2014).

[19] L. L. Rui, P. Zhang, H. Q. Huang et al., "Reputationbased incentive mechanisms in crowdsourcing," J. Electron. Inform. Technol. 38(7), 1808–1815 (2016).

[20] Y. Zhang, C. Jiang, L. Song et al., "Incentive mechanism for mobile crowdsourcing using an optimized tournament model," IEEE J. Select. Areas Commun. 35(4), 880–892 (2017).

Cui Ling-Ling, Zhang Hui. Contour reconstruction of three-dimensional spiral CT damage image[J]. Journal of Innovative Optical Health Sciences, 2018, 11(5): 1850028.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!