Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6843–6857 | Cite as

An iterative closest point approach for the registration of volumetric human retina image data obtained by optical coherence tomography

  • Xin Wang
  • Zhen-Long Zhao
  • Arlie G. Capps
  • Bernd Hamann
Article
  • 198 Downloads

Abstract

This paper introduces an improved approach for the volume data registration of human retina. Volume data registration refers to calculating out a near-optimal transformation between two volumes with overlapping region and stitching them together. Iterative closest point (ICP) algorithm is a registration method that deals with registration between points. Classical ICP is time consuming and often traps in local minimum when the overlapping region is not big enough. Optical Coherence Tomography (OCT) volume data are several separate, partially overlapping tiles. To stitch them together is a technology in computer aided diagnosis. In this paper, a new 3D registration algorithm based on improved ICP is presented. First, the Canny edge detector is applied to generate the point cloud set of OCT images. After the detection step, an initial registration method based on the feature points of the point cloud is proposed to determine an initial transformation matrix by using singular value decomposition (SVD) method. Then, an improved ICP method is presented to accomplish fine registration. Corresponding point in the point cloud is weighted to reduce the iteration times of ICP algorithm. Finally, M-estimation is used as the objective function to decrease the impact of outliers. This registration algorithm is used to process human retinal OCT volume pairs that contain an overlapping region of 75 × 500 × 375 voxels approximately. Then a comparative experiment is conducted on some public-available datasets. The experimental results show that the proposed method outperforms the classical method.

Keywords

Volume data registration Optical coherence tomography Retinal image Iterative closest point Point cloud 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Xin Wang
    • 1
    • 2
  • Zhen-Long Zhao
    • 1
  • Arlie G. Capps
    • 3
  • Bernd Hamann
    • 3
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of EducationJilin UniversityChangchunChina
  3. 3.Institute for Data Analysis and Visualization (IDAV), Department of Computer ScienceUniversity of California, DavisDavisUSA

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