Literature DB >> 11723697

Automatic localization of cephalometric Landmarks.

V Grau1, M Alcañiz, M C Juan, C Monserrat, C Knoll.   

Abstract

A system for automatic detection of cephalometric landmarks is presented. Landmark detection is carried out in two steps: a line detection module searches for significant, well-contrasted lines of the image, such as the jaw line or the nasal spine. The landmark detection module uses the lines located in the first module to determine the search areas and then applies a pattern detection algorithm, based on mathematical morphology techniques. Relations between landmarks and lines are determined by means of a training process. The system has been tested for the detection of 17 landmarks on 20 images: more than 90% of the landmarks are accurately identified.

Mesh:

Year:  2001        PMID: 11723697     DOI: 10.1006/jbin.2001.1014

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  11 in total

1.  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

Review 2.  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

3.  The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.

Authors:  Kuofeng Hung; Carla Montalvao; Ray Tanaka; Taisuke Kawai; Michael M Bornstein
Journal:  Dentomaxillofac Radiol       Date:  2019-08-14       Impact factor: 2.419

4.  Current applications and development of artificial intelligence for digital dental radiography.

Authors:  Ramadhan Hardani Putra; Chiaki Doi; Nobuhiro Yoda; Eha Renwi Astuti; Keiichi Sasaki
Journal:  Dentomaxillofac Radiol       Date:  2021-07-08       Impact factor: 2.419

5.  Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery.

Authors:  Ye-Hyun Kim; Jae-Bong Park; Min-Seok Chang; Jae-Jun Ryu; Won Hee Lim; Seok-Ki Jung
Journal:  J Pers Med       Date:  2021-04-29

6.  Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.

Authors:  Rémy Vandaele; Jessica Aceto; Marc Muller; Frédérique Péronnet; Vincent Debat; Ching-Wei Wang; Cheng-Ta Huang; Sébastien Jodogne; Philippe Martinive; Pierre Geurts; Raphaël Marée
Journal:  Sci Rep       Date:  2018-01-11       Impact factor: 4.379

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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