Abstract
This paper presents a 3D soft tissue surface reconstruction method based on improved compressed sensing and radial basis function interpolation for a small amount of uniform sampling data points on 3D surface. We adopt radial basis function interpolation to obtain the same amount of data points as to be reconstructed and propose an improved compressed sensing method to reconstruct 3D surface: we design a deterministic measurement matrix to signal observation, and then adopt the discrete cosine transform to the 3D coordinate sparse representation and use weak choose regularized orthogonal matching pursuit algorithm to reconstruct. Experimental results show that the proposed algorithm improves the resolution of the surface as well as the accuracy. The average maximum error is less than 0.9012 mm, which is smooth enough to provide accurate surface data model for virtual reality based surgery system.
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Acknowledgment
The research was supported by the National Nature Science Foundation of China (Grant No. 61372107), the National Basic Research Program of China (Grant No. 2011CB707904), and the Open Funding Project of State Key Laboratory of Virtual Technology and Systems, Beihang University (Grant No. BUAA-VR-13KF-15).
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Yu, S., Yuan, Z., Tong, Q., Liao, X., Bai, Y. (2015). Improved Compressed Sensing Based 3D Soft Tissue Surface Reconstruction. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_53
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DOI: https://doi.org/10.1007/978-3-319-24075-6_53
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