Literature DB >> 16916096

Automated 2-D cephalometric analysis on X-ray images by a model-based approach.

Weining Yue1, Dali Yin, Chengjun Li, Guoping Wang, Tianmin Xu.   

Abstract

Craniofacial landmark localization and anatomical structure tracing on cephalograms are two important ways to obtain the cephalometric analysis. In order to computerize them in parallel, a model-based approach is proposed to locate 262 craniofacial feature points, including 90 landmarks and 172 auxiliary points. In model training, 12 landmarks are selected as reference points and used to divide every training shape to 10 regions according to the anatomical knowledge; principle components analysis is employed to characterize the region shape variations and the statistical grey profile of every feature point. Locating feature points on an input image is a two-stage procedure. First, we identify the reference landmarks by image processing and pattern matching techniques, so that the shape partition is performed on the input image. Then, for each region, its feature points are located by a modified active shape model. All craniofacial anatomical structures can be traced out by connecting the located points with subdivision curves according to the prior knowledge. Users are permitted to modify the results interactively in many different ways. Experimental results show the advantage and reliability of the proposed method.

Entities:  

Mesh:

Year:  2006        PMID: 16916096     DOI: 10.1109/TBME.2006.876638

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  14 in total

1.  Accuracy and repeatability of computer aided cervical vertebra landmarking in cephalogram.

Authors:  Lili Chen; Zhicong Lan; Xiangyang Xu; Jiuxiang Lin; Huaifei Hu
Journal:  J Huazhong Univ Sci Technolog Med Sci       Date:  2012-01-27

2.  A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images.

Authors:  Abhishek Gupta; Om Prakash Kharbanda; Viren Sardana; Rajiv Balachandran; Harish Kumar Sardana
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-04-07       Impact factor: 2.924

3.  Fully automated quantitative cephalometry using convolutional neural networks.

Authors:  Sercan Ö Arık; Bulat Ibragimov; Lei Xing
Journal:  J Med Imaging (Bellingham)       Date:  2017-01-06

4.  Accuracy of computerized automatic identification of cephalometric landmarks by a designed software.

Authors:  Sh Shahidi; S Shahidi; M Oshagh; F Gozin; P Salehi; S M Danaei
Journal:  Dentomaxillofac Radiol       Date:  2013       Impact factor: 2.419

5.  Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.

Authors:  Mehmet Uğurlu
Journal:  Turk J Orthod       Date:  2022-06

6.  Automated landmark identification on cone-beam computed tomography: Accuracy and reliability.

Authors:  Ali Ghowsi; David Hatcher; Heeyeon Suh; David Wile; Wesley Castro; Jan Krueger; Joorok Park; Heesoo Oh
Journal:  Angle Orthod       Date:  2022-06-02       Impact factor: 2.684

Review 7.  Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review.

Authors:  Aravind Kumar Subramanian; Yong Chen; Abdullah Almalki; Gautham Sivamurthy; Dashrath Kafle
Journal:  Biomed Res Int       Date:  2022-06-16       Impact factor: 3.246

8.  Ceph-X: development and evaluation of 2D cephalometric system.

Authors:  Mogeeb Ahmed Ahmed Mosleh; Mohd Sapiyan Baba; Sorayya Malek; Rasheed A Almaktari
Journal:  BMC Bioinformatics       Date:  2016-12-22       Impact factor: 3.169

9.  An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images.

Authors:  Rosalia Leonardi; Daniela Giordano; Francesco Maiorana
Journal:  J Biomed Biotechnol       Date:  2009-09-10

10.  The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images.

Authors:  Shoaleh Shahidi; Ehsan Bahrampour; Elham Soltanimehr; Ali Zamani; Morteza Oshagh; Marzieh Moattari; Alireza Mehdizadeh
Journal:  BMC Med Imaging       Date:  2014-09-16       Impact factor: 1.930

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