Literature DB >> 3519070

Knowledge-based landmarking of cephalograms.

A D Lévy-Mandel, A N Venetsanopoulos, J K Tsotsos.   

Abstract

Orthodontists have defined a certain number of characteristic points, or landmarks, on X-ray images of the human skull which are used to study growth or as a diagnostic aid. This work presents the first step toward an automatic extraction of these points. They are defined with respect to particular lines which are retrieved first. The original image is preprocessed with a prefiltering operator (median filter) followed by an edge detector (Mero-Vassy operator). A knowledge-based line-following algorithm is subsequently applied, involving a production system with organized sets of rules and a simple interpreter. The a priori knowledge implemented in the algorithm must take into account the fact that the lines represent biological shapes and can vary considerably from one patient to the next. The performance of the algorithm is judged with the help of objective quality criteria. Determination of the exact shapes of the lines allows the computation of the positions of the landmarks.

Entities:  

Mesh:

Year:  1986        PMID: 3519070     DOI: 10.1016/0010-4809(86)90023-6

Source DB:  PubMed          Journal:  Comput Biomed Res        ISSN: 0010-4809


  12 in total

1.  Computer-aided automated landmarking of cephalograms.

Authors:  T Stamm; H A Brinkhaus; U Ehmer; N Meier; F Bollmann
Journal:  J Orofac Orthop       Date:  1998       Impact factor: 1.938

2.  Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network.

Authors:  Felix Kunz; Angelika Stellzig-Eisenhauer; Florian Zeman; Julian Boldt
Journal:  J Orofac Orthop       Date:  2019-12-18       Impact factor: 1.938

3.  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

4.  Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network.

Authors:  Sangmin Jeon; Kyungmin Clara Lee
Journal:  Prog Orthod       Date:  2021-05-31       Impact factor: 2.750

5.  Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms.

Authors:  Claudia Lindner; Ching-Wei Wang; Cheng-Ta Huang; Chung-Hsing Li; Sheng-Wei Chang; Tim F Cootes
Journal:  Sci Rep       Date:  2016-09-20       Impact factor: 4.379

6.  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

7.  Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting.

Authors:  Shumeng Wang; Huiqi Li; Jiazhi Li; Yanjun Zhang; Bingshuang Zou
Journal:  J Healthc Eng       Date:  2018-11-19       Impact factor: 2.682

8.  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

9.  Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines.

Authors:  Eniko T Enikov; Rein Anton
Journal:  J Med Eng       Date:  2014-10-13

Review 10.  Applications of artificial intelligence and machine learning in orthodontics: a scoping review.

Authors:  Yashodhan M Bichu; Ismaeel Hansa; Aditi Y Bichu; Pratik Premjani; Carlos Flores-Mir; Nikhilesh R Vaid
Journal:  Prog Orthod       Date:  2021-07-05       Impact factor: 2.750

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