Title of article :
3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images
Author/Authors :
Kurazume، نويسنده , , Ryo and Nakamura، نويسنده , , Kaori and Okada، نويسنده , , Toshiyuki and Sato، نويسنده , , Yoshinobu and Sugano، نويسنده , , Nobuhiko and Koyama، نويسنده , , Tsuyoshi and Iwashita، نويسنده , , Yumi and Hasegawa، نويسنده , , Tsutomu، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Pages :
10
From page :
202
To page :
211
Abstract :
In medical diagnostic imaging, the X-ray CT scanner and the MRI system have been widely used to examine 3D shapes and internal structures of living organisms and bones. However, these apparatuses are generally large and very expensive. Since an appointment is also required before examination, these systems are not suitable for urgent fracture diagnosis in emergency treatment. However, X-ray/fluoroscopy has been widely used as traditional medical diagnosis. Therefore, the realization of the reconstruction of precise 3D shapes of living organisms or bones from a few conventional 2D fluoroscopic images might be very useful in practice, in terms of cost, labor, and radiation exposure. The present paper proposes a method by which to estimate a patient-specific 3D shape of a femur from only two fluoroscopic images using a parametric femoral model. First, we develop a parametric femoral model by the statistical analysis of 3D femoral shapes created from CT images of 56 patients. Then, the position and shape parameters of the parametric model are estimated from two 2D fluoroscopic images using a distance map constructed by the Level Set Method. Experiments using synthesized images, fluoroscopic images of a phantom femur, and in vivo images for hip prosthesis patients are successfully carried out, and it is verified that the proposed system has practical applications.
Keywords :
Fluoroscopic image , registration , Medical image diagnosis , Parametric femoral model
Journal title :
Computer Vision and Image Understanding
Serial Year :
2009
Journal title :
Computer Vision and Image Understanding
Record number :
1695431
Link To Document :
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