期刊文献+

CT序列图像分割的分割及重建

Segmentation and Reconstruction of CT Sequence Image Segmentation
下载PDF
导出
摘要 目的探讨CT序列图像分割的分割及重建。方法主要结合B样条蛇线算法和模糊边缘增强算法使一些组织器官在CT图像上的二维分割得以实现,同时采用提绘制三维重建分割后的序列图像。临床实践证实,该方法具有良好的效果。程序作为子功能使用,其以Java的医学图像分析系统为基础。结果三维重建的效果对二维分割的精确度极为敏感,有跳变出现在某一幅图像或几幅图像上均会显著影响实体重建。因此,重建效果能够从一定程度上对分割的准确性进行验证。结论运用B样条蛇线算法结合模糊边缘增强算法使分割得以实现,同时运用体绘制方法三维重建分割后的序列图像。实现过程中依据实际改进了这几种算法。图像二维切片、三维重建的结果均显示了良好的效果。 Objective To investigate the segmentation and reconstruction of CT sequence image segmentation.Methods The B-spline snake line algorithm and the fuzzy edge enhancement algorithm were combined to realize the two-dimensional segmentation of some tissues and organs on CT images.At the same time,the sequence images were reconstructed by three-dimensional reconstruction.Clinical practice has confirmed that this method has a good effect.The program is used as a sub-function based on Java’s medical image analysis system.Results The effect of 3D reconstruction is extremely sensitive to the accuracy of 2D segmentation.Jumping on a certain image or several images will significantly affect the volume reconstruction.Therefore,the reconstruction effect can verify the accuracy of the segmentation to some extent.Conclusion Using the B-spline snake line algorithm combined with the fuzzy edge enhancement algorithm to achieve segmentation,the volumetric method is used to reconstruct the segmented sequence image in three dimensions.These algorithms are improved according to the actual implementation process.The results of 2D slice and 3D reconstruction of the image show that good results have been obtained.
作者 张振勇 ZHANG Zhenyong(CT Room,Lingcheng District People’s Hospital,Dezhou Shandong 253500,China)
出处 《中国继续医学教育》 2020年第16期103-105,共3页 China Continuing Medical Education
关键词 CT序列图像分割 实现 分割结果 重建 二维切片 三维重建 CT sequence image segmentation implementation segmentation result reconstruction 2D slice 3D reconstruction
  • 相关文献

参考文献11

二级参考文献65

  • 1冯林,张名举,贺明峰,戚正君.用改进的粒子群算法实现多模态刚性医学图像的配准[J].计算机辅助设计与图形学学报,2004,16(9):1269-1274. 被引量:11
  • 2尚斌,徐良贤.一种中风病人脑出血CT图像序列的自动分割方法[J].计算机工程,2004,30(B12):356-357. 被引量:1
  • 3周学成,罗锡文,刘正敏.植物根系原位CT图像分割方法的研究进展[J].计算机工程与设计,2007,28(17):4252-4256. 被引量:4
  • 4Akram MU, Khanma A, lqbal K. An automated system for liver CT enhancement and segmentation[J]. ICGST-GVIP J, 2010, 10(4): 17-22.
  • 5Lu XQ, Wu JS, Ren XY, et al. The study and application of the improved region growing algorithm for liver segmentation[J], lnt J Light Electron Opt, 2014, 125(9): 2142-2147.
  • 6Jiang HY, Cheng QS. Automatic 3D segmentation of CT images based on active contour models[C]//Computer-Aided Design and Computer Graphics, 2009 CAD/Graphics' 09 l lth IEEE Intern- ational Conference on. IEEE, 2009: 540-543.
  • 7Yang XP, Yu HC, Choi Y, et al. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points[J]. Comput Methods Programs Biomed, 2014, 113(1): 69-79.
  • 8Selver MA, Kocaoglu A, Demir GK, et al. Patient oriented and robust automatic liver segmentation for pre-evaluation of liver tran- splantation[J]. Comput Biol Med, 2008, 38(7): 765-784.
  • 9Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks[C]/fNeural Net- works, 2004. Proceedings of 2004 IEEE International Joint Con- ference, 2004: 985-990.
  • 10Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1-3): 489-501.

共引文献47

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部