Lung 4D-CT Image Registration Based on Regression Prediction
Liu Yueliang1,2, Fang Shiting1,2, Zhang Yu1,2*
1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
Abstract：To improve the low accuracy of lung 4D-CT image registration due to intensity inhomogeneity and large local deformation, a regression-based method for predicting initial deformation is presented. The core idea of this method is that the image information of different phase corresponding to the floating image is used to predict initialize deformation field. Firstly, the common registration algorithmis used to register the different phase images to the reference image to get the corresponding deformation field. Then, the image and the corresponding deformation field are divided into patches to build training set. Multi-dimensional support vector regressor is used to learn a regression model with respect to the correlation between patch appearances and their respective deformations. The floating image is then input into the regression model to achieve the initial deformation field prediction, which is used to construct intermediate image. Finally, the registration of intermediate image and reference image are refined. The experimental results show that the proposed method can effectively register lung 4D-CT images. A public dataset provided by the DIR-lab at the University of Texas M.D. Anderson Cancer Center (Houston, TX) was employed to evaluate the proposed method. Quantitative evaluation results indicate that the proposed method significantly decreases SSD (18.97±5.75,P<0.05) compared with Active Demons algorithm(49.34±23.92) and Spectral Log-Demons algorithm (31.81±15.09),significantly increases CC (0.980 ± 0.006,P<0.05) compared with Active Demons algorithm(0.952±0.022) and Spectral Log-Demons algorithm (0.967±0.015). Qualitative evaluation results also show that our approach can yield superior registration performance.
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