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2D-3D registration for 3D analysis of lower limb alignment in a weight-bearing condition 被引量:1
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作者 SHIM Eungjune KIM Youngjun +3 位作者 LEE Deukhee LEE Byung Hoon WOO Sungkyung LEE Kunwoo 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2018年第1期59-70,共12页
X-ray imaging is the conventional method for diagnosing the orthopedic condition of a patient. Computerized Tomography(CT) scanning is another diagnostic method that provides patient’s 3D anatomical information. Howe... X-ray imaging is the conventional method for diagnosing the orthopedic condition of a patient. Computerized Tomography(CT) scanning is another diagnostic method that provides patient’s 3D anatomical information. However, both methods have limitations when diagnosing the whole leg; X-ray imaging does not provide 3D information, and normal CT scanning cannot be performed with a standing posture. Obtaining 3D data regarding the whole leg in a standing posture is clinically important because it enables 3D analysis in the weight bearing condition.Based on these clinical needs, a hardware-based bi-plane X-ray imaging system has been developed; it uses two orthogonal X-ray images. However, such methods have not been made available in general clinics because of the hight cost. Therefore, we proposed a widely adaptive method for 2 D X-ray image and 3D CT scan data. By this method, it is possible to threedimensionally analyze the whole leg in standing posture. The optimal position that generates the most similar image is the captured X-ray image. The algorithm verifies the similarity using the performance of the proposed method by simulation-based experiments. Then, we analyzed the internal-external rotation angle of the femur using real patient data. Approximately 10.55 degrees of internal rotations were found relative to the defined anterior-posterior direction. In this paper, we present a useful registration method using the conventional X-ray image and 3D CT scan data to analyze the whole leg in the weight-bearing condition. 展开更多
关键词 2d-3d registration 3d analysis X-RAY CT simulated annealing
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A new projection model based robust 2D-3D registration method on Fourier-Mellin space for image guided intervention
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作者 魏嵬 Jia Kebin 《High Technology Letters》 EI CAS 2013年第4期378-383,共6页
An automatic method is proposed to solve the registration problem,which aligns a single 2D fluoroscopic image to a 3D image volume without demanding any additional media like calibration plate or user interactions.Fir... An automatic method is proposed to solve the registration problem,which aligns a single 2D fluoroscopic image to a 3D image volume without demanding any additional media like calibration plate or user interactions.First,a mathematic projection model is designed which can reduce the influence of projection distortion on parameter optimization and improve the registration accuracy.Then,a two stage optimization method is proposed,which enables a robust registration in a wide parameter space.Furthermore,an automatic registration framework is proposed based on the FourierMellin robust image comparison descriptor.Experimental results show that the registration method has a high accuracy with average rotation error of 0.6 degree and average translation error of 1.4mm. 展开更多
关键词 image guided surgery 2d-3d registration digitally reconstructed radiograph dRR) FFT
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Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning
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作者 Cong Wang Shuaining Xie +4 位作者 Kang Li Chongyang Wang Xudong Liu Liang Zhao Tsung-Yuan Tsai 《Engineering》 SCIE EI 2021年第6期881-888,共8页
Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are i... Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are insufficient data for training,the data-driven approach will fail.We propose a feature-based transfer-learning method to extract features from fluoroscopic images.With three subjects and fewer than 100 pairs of real fluoroscopic images,we achieved a mean registration success rate of up to 40%.The proposed method provides a promising solution,using a learning-based registration method when only a limited number of real fluoroscopic images is available. 展开更多
关键词 2d3d registration Machine learning domain adaption point correspondence
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