Abstract
In this paper, we proposed a novel non-rigid 2D-3D registration framework, which is based on Support Vector Regression (SVR) to compensate the disadvantages of generating large amounts of Digitally Rendered Radiographs (DRRs) in the stage of intra-operation for radiotherapy. It is successfully used to estimate similarity metric distribution from prior sparse target metric values against different featured transforming parameters of non-rigid registration. Through applying the appropriate selected features and kernel of SVR solution to our registration framework, experiments provide a precise registration result efficiently in order to assist radiologists locating the accurate positions of radiation surgery. Meanwhile, a medical diagnosis database is also built up to reduce the therapy cost and accelerate the procedure of radiotherapy in the case of future scheduling of multiple treatments.
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Qi, W., Gu, L., Xu, J. (2008). Non-rigid 2D-3D Registration Based on Support Vector Regression Estimated Similarity Metric. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_37
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DOI: https://doi.org/10.1007/978-3-540-79982-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79981-8
Online ISBN: 978-3-540-79982-5
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