3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

被引:29
|
作者
Kurazume, Ryo [1 ]
Nakamura, Kaori [1 ]
Okada, Toshiyuki [2 ]
Sato, Yoshinobu [3 ]
Sugano, Nobuhiko [3 ]
Koyama, Tsuyoshi [4 ]
Iwashita, Yumi [1 ]
Hasegawa, Tsutomu [1 ]
机构
[1] Kyushu Univ, Grad Sch Informat Sci & Elect Engn, Nishi Ku, Fukuoka 8190395, Japan
[2] Osaka Univ Hosp, Med Ctr Translat Res, Suita, Osaka 5650871, Japan
[3] Osaka Univ, Grad Sch Med, Suita, Osaka 5650871, Japan
[4] Natl Hosp Org, Osaka Minami Med Ctr, Dept Orthopaed Surg, Osaka 5868521, Japan
关键词
Fluoroscopic image; Parametric femoral model; Registration; Medical image diagnosis; REGISTRATION; ALGORITHM;
D O I
10.1016/j.cviu.2008.08.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:202 / 211
页数:10
相关论文
共 50 条
  • [41] A reconstruction method of 3D face model from front and side 2D face images using deep learning model
    Nishio, Ryota
    Oono, Masaki
    Goto, Takaharu
    Kishimoto, Takahiro
    Shishibori, Masami
    [J]. FIFTEENTH INTERNATIONAL CONFERENCE ON QUALITY CONTROL BY ARTIFICIAL VISION, 2021, 11794
  • [42] Approach to 3D reconstruction of calcareous sand using 2D images of multi-view
    Hu Cong
    Long Zhi-lin
    Kuang Du-min
    Gong Zhao-mao
    Yu Piao-yi
    Xu Guo-bin
    [J]. ROCK AND SOIL MECHANICS, 2022, 43 (03) : 761 - 768
  • [43] 3D surface reconstruction from multiview photographic images using 2D edge contours
    Prakoonwit, Simant
    Benjamin, Ralph
    [J]. 3D RESEARCH, 2012, 3 (04):
  • [44] 3D reconstruction and navigated removal of femoral bone cement in revision THR based on few fluoroscopic images
    de la Fuente, M
    Schkommodau, E
    Lutz, P
    Neuss, M
    Wirtz, DC
    Radermacher, K
    [J]. CARS 2004: COMPUTER ASSISTED RADIOLOGY AND SURGERY, PROCEEDINGS, 2004, 1268 : 626 - 631
  • [45] Using specularities in comparing 3D models and 2D images
    Osadchy, Margarita
    Jacobs, David
    Ramamoorthi, Ravi
    Tucker, David
    [J]. COMPUTER VISION AND IMAGE UNDERSTANDING, 2008, 111 (03) : 275 - 294
  • [46] Using 2D CT images to directly design and print 3D parametric porous medical models
    Wang, Zhiping
    Millet, Dominique
    Zhang, Yicha
    [J]. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2023, 72 (01) : 117 - 120
  • [47] Individualized 3D Face Model Reconstruction using Two Orthogonal Face Images
    Weon, SunHee
    Joo, SungIl
    Choi, HyungIl
    [J]. WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, WCECS 2012, VOL I, 2012, : 680 - 684
  • [48] A 3D Model Reconstruction Method Using Slice Images
    LI Hong-an
    KANG Bao-sheng
    [J]. CADDM, 2013, (03) : 18 - 22
  • [49] 3D shape reconstruction of bone from two x-ray images using 2D/3D non-rigid registration based on moving least-squares deformation
    Cresson, T.
    Branchaud, D.
    Chav, R.
    Godbout, B.
    de Guise, J. A.
    [J]. MEDICAL IMAGING 2010: IMAGE PROCESSING, 2010, 7623
  • [50] Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
    Wei, Zizhuang
    Wang, Yao
    Yi, Hongwei
    Chen, Yisong
    Wang, Guoping
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (04):