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An efficient method for estimating soft tissue deformation based on intraoperative stereo image features and point‐based registration
Author(s) -
Ahmadian Alireza,
Serej Nasim Dadashi,
Karimifard Saeed,
Farnia Parastoo
Publication year - 2013
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22064
Subject(s) - point cloud , iterative closest point , projection (relational algebra) , computer science , computer vision , deformation (meteorology) , artificial intelligence , point (geometry) , soft tissue , image registration , control point , mathematics , algorithm , image (mathematics) , geometry , geology , radiology , medicine , oceanography
Estimation of soft tissue deformation occurring during image‐guided surgery using an easily implemented and accurate method is necessary. Using a stereo camera, this study focuses on two efficient methods for estimating soft tissue deformation. Two methods were proposed to overcome limitations associated with the typical methods used for estimating soft tissue deformation, such as dependence on accuracy of the operator and indentation of skin. The first method is based on Triclops SDK, and the second method is based on projecting a pattern to acquire P‐Lands (Projected Landmarks). Based on the proposed methods, surface information is acquired in the form of point clouds of surface point coordinates to the submillimeter accuracy. The reconstructed predeformation three‐dimensional (3D) point cloud obtained for each method is registered with a modified iterative closest point algorithm to a postdeformation 3D point cloud obtained from the same region of interest. Results were compared with an MRI–MRI registration method as a control. Results are provided as RMS differences between the initial and final coordinates of corresponding points. The average RMS difference for the typical method is 3.53 mm, that for the Triclops SDK method is 2.32 mm, and that for the P‐Lands projection method is 2.06 mm. The MRI‐MRI registration had an average RMS difference of 1.12 mm. Using MRI‐MRI registration as the gold standard, the average error obtained for the typical method was 2.41 mm, that for the first method was 1.2 mm, and that for the second method was 0.94 mm. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 294–303, 2013