A Novel Automatic Registration Method for Fluorescein Fundus Angiography Sequences Based on Mutual Information

Xiaoyan LIU; Haohao WANG; Gang SUN; Pu ZHANG; Min LIU; Ling GAO

doi:10.11999/JEIT170868

Volume 40 Issue 8

Aug. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(8): 1919-1926

Xiaoyan LIU, Haohao WANG, Gang SUN, Pu ZHANG, Min LIU, Ling GAO. A Novel Automatic Registration Method for Fluorescein Fundus Angiography Sequences Based on Mutual Information[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1919-1926. doi: 10.11999/JEIT170868

Citation:

Xiaoyan LIU, Haohao WANG, Gang SUN, Pu ZHANG, Min LIU, Ling GAO. A Novel Automatic Registration Method for Fluorescein Fundus Angiography Sequences Based on Mutual Information[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1919-1926. doi: 10.11999/JEIT170868

Citation:

Xiaoyan LIU, Haohao WANG, Gang SUN, Pu ZHANG, Min LIU, Ling GAO. A Novel Automatic Registration Method for Fluorescein Fundus Angiography Sequences Based on Mutual Information[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1919-1926. doi: 10.11999/JEIT170868

PDF( 3054 KB)

A Novel Automatic Registration Method for Fluorescein Fundus Angiography Sequences Based on Mutual Information

doi: 10.11999/JEIT170868

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2.
Department of Ophthalmology, the Second Xiangya hospital of Central South University, Changsha 410011, China

Received Date: 2017-09-14
Rev Recd Date: 2018-05-09

Available Online: 2018-06-07

Publish Date: 2018-08-01

Abstract

Abstract

Fluorescein Fundus Angiography (FFA) is regarded as the golden diagnostic criteria for fundus diseases. However, dislocation or rotation of the interested images on anatomic landmark (like retinal vascular branches, neovascularization), caused by inevitable eyeball movement, brings about difficulties in subsequent quantitative analysis and progress assessment of the diseases. In order to solve the above problems, a novel method based on mutual information is proposed for automatic registration of FFA image sequence. Firstly, the vessels of image sequence are segmented by multi-scale linear filter and down sampled hereafter by image pyramid. Then, the similarity of sampled images is calculated by mutual information and the evolution strategy is adopted to optimize the registration parameters. Finally, the transformation matrix with maximum mutual information is obtained to register the FFA image. Tests with FFA image sequences of 4 patients (total 1039 frames) show that the overall registration rate of the algorithm reaches 93% and the failure rate is only 1%. Compared with the classical registration methods, the proposed method shows better comprehensive performance in terms of registration rate, computing speed as well as robustness. It lays basic foundations for quantitative analysis on FFA images and potential clinical application.
- Fluorescein Fundus Angiography (FFA),
- Image sequence,
- Mutual information,
- Vascular segmentation

FullText(HTML)

References(23)

References

DREO J, NUNES J C, and SIARRY P. Robust rigid registration of retinal angiograms through optimization[J]. Computerized Medical Imaging & Graphics, 2006, 30(8): 453－463. DOI: 10.1016/j.compmedi mag.2006.07.004.

LAAKSONEN L, CLARIDGE E, FÄLT P, et al. Comparison of image registration methods for composing spectral retinal images[J]. Biomedical Signal Processing & Control, 2017, 36: 234－245. DOI: 10.1016/j.bspc.2017.03.003.

COPELAND A, MANGOUBI R, DESAI M, et al. Enhancing the surgeons reality: Smart visualization of bolus time of arrival and blood flow anomalies from time lapse series for safety and speed of cerebrovascular surgery[C]. IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 2009: 1–4.

GHASSABI Z, SHANBEHZADEH J, and MOHAMMADZADEH A. A structure-based region detector for high-resolution retinal fundus image registration[J]. Biomedical Signal Processing & Control, 2016, 23: 52－61.DOI: 10.1016/j.bspc.2015.08.005

PEREZROVIRA A, CABIDO R, TRUCCO E, et al. RERBEE: Robust efficient registration via bifurcations and elongated elements applied to retinal fluorescein angiogram sequences[J]. IEEE Transactions on Medical Imaging, 2012, 31(1): 140－150. DOI: 10.1109/TMI.2011.2167517.

TSAI C L, LI Chunyi, YANG Gehua, et al. The edge-driven dual-bootstrap iterative closest point algorithm for registration of multimodal fluorescein angiogram sequence[J]. IEEE Transactions on Medical Imaging, 2010, 29(3): 636－649. DOI: 10.1109/TMI.2009.2030324.

汪立, 蒋念平. 基于改进Harris角点检测的视网膜图像配准[J]. 电子科技, 2017, 30(2): 119–122. DOI: 10.16180/j.cnki.issn1007－7820.2017.02.031.

WANG Li and JIANG Nianping. Retinal image registration based on improved Harris corner detection[J]. Electronic Science and Technology, 2017, 30(2): 119–122. DOI: 10.16180/j.cnki.iss n1007－7820.2017.02.031.

YAVUZ Z and KÖSE C. Retinal fundus image registration using bifurcation and crossover points[C]. Signal Processing and Communication Application Conference, Zonguldak, Turkey, 2016: 1485–1488.

PALRAJ P and VENNILA I. Retinal fundus image registration via blood vessel extraction using binary particle swarm optimization[J]. Journal of Medical Imaging & Health Informatics, 2016, 6(2): 328－337. DOI: 10.1166/jmihi.2016.1701

SAHA S K, DI X, FROST S, et al. A two-step approach for longitudinal registration of retinal images[J]. Journal of Medical Systems, 2016, 40: 277. DOI: 10.1007/s10916-016-0640-0.

CAN A, STEWART C V, TANENBAUM H L, et al. A Feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(3): 347－364. DOI: 10.1109/34.990136.

李英杰, 张俊举, 常本康, 等. 电子与信息学报, 2016. 38(1): 8－14. DOI: 10.11999/JEIT150479.

LI Yingjie, ZHANG Junju, CHANG Benkang, et al. A multi - band infrared image joint registration and fusion method[J]. Journal of Electronics & Information Technology, 2016. 38(1): 8－14. DOI: 10.11999/JEIT150479.

HENEGHAN C, MAGUIRE P, RYAN N, et al. Retinal image registration using control points[C]. Proceedings IEEE International Symposium on Biomedical Imaging, Washington, DC, USA, 2002: 349–352. doi: 10.1109/ISBI. 2002.1029265.

刘妍, 余淮, 杨文, 等. 利用SAR-FAST角点检测的合成孔径雷达图像配准方法[J]. 电子与信息学报, 2017, 39(2): 430–436. DOI: 10.11999/JEIT160386.

LIU Yan, YU Huai, YANG Wen, et al. Synthetic aperture radar image registration method using SAR - FAST corner detection [J]. Journal of Electronics & Information Technology, 2017, 39(2): 430–436. DOI: 10.11999/JEIT160386.

LALIBERTÉ F, GAGNON L, and SHENG Y. Registration and fusion of retinal images--an evaluation study[J]. IEEE Transactions on Med Imaging, 2003, 22(5): 661－673. DOI: 10.1109/TMI.2003.812263.

任克强, 胡梦云. 基于改进SURF算子的彩色图像配准算法[J]. 电子测量与仪器学报, 2016, 30(5): 748－756. DOI: 10.13382/j.jemi.2016.05.011.

REN Keqiang and HU Mengyun. Color image registration algorithm based on improved SURF operator[J]. Journal of Electronic Measurement and Instrument, 2016, 30(5): 748－756. DOI: 10.13382/j.jemi.2016.05.011.

CHEN Jian, TIAN Jie, LEE N, et al. A partial intensity invariant feature descriptor for multimodal retinal image registration[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(7): 1707－1718. DOI: 10.1109/TBME.2010.2042169.

WANG Guang, WANG Zhicheng, CHEN Yufei, et al. Robust point matching method for multimodal retinal image registration[J]. Biomedical Signal Processing & Control, 2015, 19: 68－76. DOI: 10.1016/j.bspc.2015.03.004.

YANG G, STEWART C V, SOFKA M, et al. Registration of challenging image pairs: initialization, estimation, and decision[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(11): 1973－1989. DOI: 10.1109/TPAMI.2007.1116.

NUNES J C and BOUAOUNE Y. A multiscale elastic registration scheme for retinal angiograms[J]. Computer Vision & Image Understanding, 2004, 95(2): 129－149. DOI: 10.1016/j.cviu.2004.03.007.

NGUYEN U T V, BHUIYAN A, PARK L A F, et al. An effective retinal blood vessel segmentation method using multi-scale line detection[J]. Pattern Recognition, 2013, 46(3): 703－715. DOI: 10.1016/j.patcog.2012.08.009.

STYNER M, BRECHBÜHLER C, SZCKELY G, et al. Parametric estimate of intensity inhomogeneities applied to MRI[J]. IEEE Transactions on Medical Imaging, 2000, 19(3): 153－165. DOI: 10.1109/42.845174.

GLOCKER B, KOMODAKIS N, PARAGIOS N, et al. Inter and intra-modal deformable registration: Continuous deformations meet efficient optimal linear programming[C]. IPMI'07 Processings of the 20th International Conference on Information Processing in Medical Imaging, Kerkrade, The Netherlands, 2007: 408–420.

Relative Articles

Supplements(0)

Cited By

Proportional views