Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 24, Issue -, Pages 25-33Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2015.09.005
Keywords
Optical flow; Deformable image registration; Local binary pattern; 4D CT
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Funding
- National Natural Science Foundation of China [81371635, 11401346]
- Research Fund for the Doctoral Program of Higher Education of China
- Ministry of Education of China [20120131110062]
- Shandong Province Science and Technology Development Plan [2013GGX10104]
- Natural Science Foundation of Shandong Province [ZR2014FM034]
- Promotive Research Fund for Excellent Young and Middle-Aged Scientists of Shandong Province [BS2014DX012]
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Deformable image registration remains a challenging research area due to difficulties associated with local intensity variation and large motion. In this paper, an Accurate Inverse-consistent Symmetric Optical Flow (AISOF) method is proposed to overcome these difficulties. The two main contributions of AISOF include the following: (1) a coarse-to-fine strategy for an inverse-consistent symmetric method and (2) a novel Hybrid Local Binary Pattern (HLBP) to the classical Lucas-Kanade optical flow method. The HLBP consists of a median binary pattern and a generalised centre-symmetric local binary pattern. The generalised centre-symmetric local binary pattern has two thresholds, and this pattern can capture more information than the classical centre-symmetric local binary pattern, which has one threshold. The proposed HLBP can cope well with high contrast intensity and local intensity variation. Because the inverse-consistent symmetric method can reduce inverse consistency errors in Markov random fields based registration methods, we adopted this method to improve the accuracy of registration. In addition, a coarse-to-fine strategy was adopted to handle large motion. The proposed AISOF method was evaluated for 10 publicly available 4D CT lung datasets from the DIR-Lab. The mean target registration error of the AISOF method is 1.16 mm, which is significantly superior to the error of the classical Lucas-Kanade optical flow method, 2.83 mm. Moreover, this error is also the smallest of all unmasked registration methods using these datasets. (C) 2015 Elsevier Ltd. All rights reserved.
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