Non-rigid 2D-3D Registration Based on Support Vector Regression Estimated Similarity Metric

Authors:
Wenyuan Qi;Lixu Gu;Jianrong Xu
Affiliations:
Computer Science, Shanghai Jiaotong University, Shanghai, China 200240;Computer Science, Shanghai Jiaotong University, Shanghai, China 200240;Computer Science, Shanghai Jiaotong University, Shanghai, China 200240 and Shanghai Renji Hospital, Shanghai
Venue:
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Year:
2008

Citing 4
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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.