3D RECONSTRUCTION FROM MULTI-VIEW MEDICAL X-RAY IMAGES - REVIEW AND EVALUATION OF EXISTING METHODS

被引:10
|
作者
Hosseinian, S. [1 ]
Arefi, H. [1 ]
机构
[1] Univ Tehran, Sch Surveying & GeoSpatial Engn, Tehran, Iran
关键词
Stereoradiography; 3D Reconstruction; X-ray images; close range photogrammetry; SCOLIOTIC VERTEBRAE; NSCP TECHNIQUE; MODELS; SPINE; REGISTRATION; RADIOGRAPHY; VALIDATION; POINTS; PELVIS; LIMB;
D O I
10.5194/isprsarchives-XL-1-W5-319-2015
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The 3D concept is extremely important in clinical studies of human body. Accurate 3D models of bony structures are currently required in clinical routine for diagnosis, patient follow-up, surgical planning, computer assisted surgery and biomechanical applications. However, 3D conventional medical imaging techniques such as computed tomography (CT) scan and magnetic resonance imaging (MRI) have serious limitations such as using in non-weight-bearing positions, costs and high radiation dose(for CT). Therefore, 3D reconstruction methods from biplanar X-ray images have been taken into consideration as reliable alternative methods in order to achieve accurate 3D models with low dose radiation in weight-bearing positions. Different methods have been offered for 3D reconstruction from X-ray images using photogrammetry which should be assessed. In this paper, after demonstrating the principles of 3D reconstruction from X-ray images, different existing methods of 3D reconstruction of bony structures from radiographs are classified and evaluated with various metrics and their advantages and disadvantages are mentioned. Finally, a comparison has been done on the presented methods with respect to several metrics such as accuracy, reconstruction time and their applications. With regards to the research, each method has several advantages and disadvantages which should be considered for a specific application.
引用
收藏
页码:319 / 326
页数:8
相关论文
共 50 条
  • [21] Cortical Volumetry using 3D Reconstruction of Metacarpal Bone from Multi-view Images
    Jayakar, Avinash D.
    Sambath, Gautham
    Areeckal, Anu Shaju
    David, Sumam S.
    2018 IEEE RECENT ADVANCES IN INTELLIGENT COMPUTATIONAL SYSTEMS (RAICS), 2018, : 79 - 83
  • [22] 3D building model reconstruction from multi-view aerial images and LiDAR data
    Cheng, Liang
    Gong, Jianya
    Li, Manchun
    Liu, Yongxue
    Song, Xiaogang
    Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 2009, 38 (06): : 494 - 501
  • [23] Automatic 3D building reconstruction from multi-view aerial images with deep learning
    Yu, Dawen
    Ji, Shunping
    Liu, Jin
    Wei, Shiqing
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 171 : 155 - 170
  • [24] 3D reconstruction of volume defects from few X-ray images
    Lehr, C
    Liedtke, CE
    COMPUTER ANALYSIS OF IMAGES AND PATTERNS, 1999, 1689 : 275 - 284
  • [25] 3D Texture Mapping in Multi-view Reconstruction
    Chen, Zhaolin
    Zhou, Jun
    Chen, Yisong
    Wang, Guoping
    ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT I, 2012, 7431 : 359 - 371
  • [26] 3D Reconstruction with Multi-view Texture Mapping
    Ye, Xiaodan
    Wang, Lianghao
    Li, Dongxiao
    Zhang, Ming
    NEURAL INFORMATION PROCESSING (ICONIP 2017), PT III, 2017, 10636 : 198 - 207
  • [27] Multi-View Stereo 3D Edge Reconstruction
    Bignoli, Andrea
    Romanoni, Andrea
    Matteucci, Matteo
    2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018), 2018, : 867 - 875
  • [28] PDE-Based 3D Surface Reconstruction from Multi-View 2D Images
    Zhu, Zaiping
    Iglesias, Andres
    Zhou, Liqi
    You, Lihua
    Zhang, Jianjun
    MATHEMATICS, 2022, 10 (04)
  • [29] 3D Concept Learning and Reasoning from Multi-View Images
    Hong, Yining
    Lin, Chunru
    Du, Yilun
    Chen, Zhenfang
    Tenenbaum, Joshua B.
    Gan, Chuang
    2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 9202 - 9212
  • [30] PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
    Liu, Jiachen
    Ji, Pan
    Bansal, Nitin
    Cai, Changjiang
    Yan, Qingan
    Huang, Xiaolei
    Xu, Yi
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2022, : 8655 - 8665