Retinal Image Registration Using Geometrical Features

被引:25
|
作者
Gharabaghi, Sara [1 ]
Daneshvar, Sabalan [1 ]
Sedaaghi, Mohammad Hossein [1 ]
机构
[1] Sahand Univ Technol, Fac Elect Engn, Tabriz, Iran
关键词
Image registration; Retinal images; Affine moment invariant (AMI); Affine transformation; OPTIC DISC; MODEL; ALGORITHM; FUSION; FOVEA;
D O I
10.1007/s10278-012-9501-7
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.
引用
收藏
页码:248 / 258
页数:11
相关论文
共 50 条
  • [31] Global registration of overlapping images using accumulative image features
    Krish, Karthik
    Heinrich, Stuart
    Snyder, Wesley E.
    Cakir, Halil
    Khorram, Siamak
    [J]. PATTERN RECOGNITION LETTERS, 2010, 31 (02) : 112 - 118
  • [32] Multi-Modal Image Registration Using Structural Features
    Kasiri, Keyvan
    Clausi, David A.
    Fieguth, Paul
    [J]. 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 5550 - 5553
  • [33] Vector Features for Image Matching and Image Registration
    Liang, Jianning
    Zhou, Yan
    [J]. FIFTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2013), 2014, 9069
  • [34] ROBUST RETINAL IMAGE REGISTRATION USING EXPECTATION MAXIMISATION WITH MUTUAL INFORMATION
    Reel, Parminder Singh
    Dooley, Laurence S.
    Wong, K. C. P.
    Boerner, Anko
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 1118 - 1122
  • [35] Automatic Retinal Image Registration Using fully Connected Vascular Tree
    Parekar, Janabai
    Porwal, Prasanna
    Kokare, Manesh
    [J]. 2016 INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (ICONSIP), 2016,
  • [36] Geometrical regularization of displacement fields for histological image registration
    Pitiot, Alain
    Guimond, Alexandre
    [J]. MEDICAL IMAGE ANALYSIS, 2008, 12 (01) : 16 - 25
  • [37] Urban green space remote sensing image registration using image mixed features
    Gao, Xue-Yan
    Pan, An-Ning
    Yang, Yang
    [J]. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2019, 53 (06): : 1205 - 1217
  • [38] Image Registration With Artificial Neural Networks Using Spatial and Frequency Features
    Gadde, Pramod
    Yu, Xiao-Hua
    [J]. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4643 - 4649
  • [39] Remote Sensing Image Registration Using Convolutional Neural Network Features
    Ye, Famao
    Su, Yanfei
    Xiao, Hui
    Zhao, Xuqing
    Min, Weidong
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (02) : 232 - 236
  • [40] Diffusion tensor image registration using hybrid connectivity and tensor features
    Wang, Qian
    Yap, Pew-Thian
    Wu, Guorong
    Shen, Dinggang
    [J]. HUMAN BRAIN MAPPING, 2014, 35 (07) : 3529 - 3546