Slice interpolation on multilevel modified curvature-based registration

WANG Xin-zheng; BU Xiong-zhu; YU Jing

doi:10.3788/OPE.20162405.1224

您当前的位置：

首页 >

文章列表页 >

Slice interpolation on multilevel modified curvature-based registration

更新时间：2020-08-13

- Slice interpolation on multilevel modified curvature-based registration
- Optics and Precision Engineering Vol. 24, Issue 5, Pages: 1224-1231(2016)
- 作者机构：
  
  南京理工大学机械工程学院,江苏南京,中国,210094
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162405.1224
  CLC： TP391.4
- Received：11 January 2016，
  
  Revised：04 March 2016，
  
  Published：25 May 2016
- 稿件说明：
移动端阅览
王新征, 卜雄洙, 于靖. 结合多分辨率修正曲率配准的层间插值[J]. 光学精密工程, 2016,24(5): 1224-1231

WANG Xin-zheng, BU Xiong-zhu, YU Jing. Slice interpolation on multilevel modified curvature-based registration[J]. Editorial Office of Optics and Precision Engineering, 2016,24(5): 1224-1231
王新征, 卜雄洙, 于靖. 结合多分辨率修正曲率配准的层间插值[J]. 光学精密工程, 2016,24(5): 1224-1231 DOI： 10.3788/OPE.20162405.1224.

WANG Xin-zheng, BU Xiong-zhu, YU Jing. Slice interpolation on multilevel modified curvature-based registration[J]. Editorial Office of Optics and Precision Engineering, 2016,24(5): 1224-1231 DOI： 10.3788/OPE.20162405.1224.

摘要

针对体数据各坐标轴分辨率不一致

导致医学及工业三维图像重建时出现边界台阶状结构、细节断裂或缺失等问题

提出了基于多分辨率修正曲率配准的层间插值方法。该方法采用反投影重建形式增强图像细节

解决配准图像清晰度和对比度弱的问题;利用三次卷积插值构造切片的低分辨率图像以保留图像细微结构

提高配准精度。采取从低分辨率粗配准到高分辨率精细配准的策略减少计算时间

提高计算效率。利用切片图像对应像素间存在对称形变结构的特征

建立了修正曲率模型估计形变场

解决了配准时单向形变不一致的问题。最后

通过离散余弦变换(DCT)的数值解析方案对构建的形变场估计函数进行优化

利用最终形变场数据对切片进行线性插值

计算出层间图像。实验结果表明

提出的算法能够消除现有方法插值图像的边缘模糊现象。与线性插值算法相比

提出方法的均方差(MSD)减小了40%

高于对称曲率模型

且耗时仅为该模型的20%左右

满足应用要求。

Abstract

When 3D medical and industrial images are reconstructed

the differences of volume data resolution in three directions often lead to detail missing and surface discontinuous. Therefore

a slice interpolation algorithm was proposed based on multilevel modified curvature-based registration. With the method

a back projection method was used to enhance the image details and to improve the definition and contrast of the matched images. Then

the cubic convolution interpolation was used to construct a low resolution image for preserving image detail and to improve the matching accuracy. A new scheme from low resolution coarse registration to high resolution fine registration was proposed to reduce the calculation time to improve the calculation efficiency. Furthermore

a modified curvature-based registration model was built based on the symmetric transformation between the pixels in the slice images to resolve the problem of transformation inconsistency in image matching. Finally

the Discrete Cosine Transform

(DCT)was used to minimize and optimize the joint criterion of a deformation field and the deformation field data were used to perform the linear interpolation for the slice to obtain the slice image. Experiment results show the proposed method eliminates the blurred edges of the slice interpolation image. As compared with the linear interpolation method

the Mean Square Deviation(MSD) of the proposed method has reduced by 40%

which is higher than that of the classical curvature mode and the time consuming also is 20% that of the classical one.

关键词

Keywords

references

张晓梦, 张涛. 基于FPGA实现CT图像重建加速的设计[J]. 液晶与显示, 2014, 29(3):455-460. ZHANG X M, ZHANG T. Design of CT image reconstruction acceleration based on FPGA[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(3):455-460. (in Chinese)

李铭, 张涛, 郑健,等. 基于切线反投影的CT金属位置和形状标定[J]. 液晶与显示, 2013, 28(2):295-299. LI M, ZHANG T, ZHENG J,et al.. Determination of location and shape of metallic object in CT based on tangent back-projection[J]. Chinese Journal of Liquid Crystals and Displays, 2013, 28(2):295-299. (in Chinese)

BAGHAIE A, YU Z. An optimization method for slice interpolation of medical images[J]. Journal of Physiology, 2014, 591(6):1447-1461.

GOSHTASBY A, TUMER D A, ACKERMAN L V. Matching of tomographic slices for interpolation[J]. IEEE Trans. on Medical Imaging, 1992, 11(4), 507-516.

王雷, 高欣, 崔学理,等. 基于灰度距离融合的2D/3D刚性配准[J]. 光学精密工程, 2014, 22(10):2815-2824. WANG L, GAO X, CUI X L,et al.. 2D/3D rigid registration by integrating intensity distance[J]. Opt. Precision Eng., 2014, 22(10):2815-2824. (in Chinese)

王健博,朱明. 基于字典描述向量的实时图像配准[J]. 光学精密工程, 2014, 22(6):1613-1621. WANG J B, ZHU M. Real time image registration based on dictionary feature descriptor[J]. Opt. Precision Eng., 2014, 22(6):1613-1621. (in Chinese)

何林阳, 刘晶红, 李刚,等. 改进BRISK特征的快速图像配准算法[J]. 红外与激光工程, 2014,43(8):2722-2727. HE L Y, LIU J H, LI G, et al.. Fast image registration approach based on improved BRISK[J].Infrared and Laser Engineering, 2014,43(8):2722-2727. (in Chinese)

PENNEY G P, SCHNABEL J A, RUECKERT D, et al.. Registration based interpolation[J]. IEEE Trans. on Medical Imaging, 2004, 23(7), 922-926.

周志勇, 薛维琴, 郑健,等. 基于t分布混合模型的点集非刚性配准算法[J]. 光学精密工程, 2013, 21(9):2405-2420. ZHOU ZH Y, XUE W Q, ZHENG J, et al... Point set non-rigid registration using t-distribution mixture model[J].Opt. Precision Eng., 2013, 21(9):2405-2420. (in Chinese)

季尔优, 顾国华, 柏连发,等. 前景重配准的改进帧间误差最小化非均匀性校正算法[J]. 红外与激光工程, 2014,43(5):1672-1678. JI E Y, GU G H, BAI L F, et al.. Improved interframe registration based least-mean-square-error non-uniformity correction algorithm by foreground re-registration[J]. Infrared and Laser Engineering, 2014,43(5):1672-1678. (in Chinese)

FRAKES D H, DASI L P, PEKKAN K, et al.. A new method for registration-based medical image interpolation[J].IEEE Trans. on Medical Imaging, 2008:27(3), 370-377.

LENG J, XU G L, ZHANG Y. Medical image interpolation based on multi-resolution registration[J].Computers & Mathematics with Applications, 2013,66(1):1-18.

BAGHAIE A, YU Z. Curvature-based registration for slice interpolation of medical images[J]. Computational Modeling of Objects Presented in Images, Fundamentals, Methods, and Applications Springer International Publishing, 2014:69-80.

FISCHER B, MODERSITZKI J. A unified approach to fast image registration and a new curvature based registration technique[J]. Linear Algebra and its Applications, 2004,380:107-124.

MODERSITZKI J.FAIR:flexible algorithms for image registration, vol. 6. SIAM (2009)

HELLIER P, BARILLOT C, MEMIN E, et al.. Hierarchical estimation of a dense deformation field for 3-d robust registration[J]. IEEE Transaction on Medical Imaging, 2001, 20(5):388-402.

Courtesy of Harvard Medical School,Normal aging:structure and function[OL]. http://www.med.harvard.edu/AANLIB/home.html.

ZHANG X, FENG X, WANG W, et al.. Edge strength similarity for image quality assessment[J]. IEEE Signal Processing Letters, 2013, 20(4):319-322.

Views

273

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Infrared-visible remote sensing image registration method based on Kullback-Leibler divergence using variational approximation

Multispectral image fusion method for surface defect detection of IC devices

Image reconstruction based on deep compressive sensing combined with global and local features

Lens-less imaging via score-based generative model

Hyperspectral reconstruction from RGB images based on Res2-Unet deep learning network

Related Author

WANG Jia

WU Hao

FU Ruigang

KONG Lingshuang

ZUO Yi

DENG Yaohua

HUANG Zhihai

ZHONG Yuanhong

Related Institution

College of Electronic Science and Technology， National University of Defense Technology

Institute of Dataspace， Hefei Comprehensive National Science Center

School of Electronic Information and Electrical Engineering， Changsha University

College of Mechanical and Electrical Engineering， Guangdong University of Technology

Institute of Physical Science and Information Technology， Anhui University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰