Literature DB >> 2721174

Automatic landmarking of cephalograms.

S Parthasarathy1, S T Nugent, P G Gregson, D F Fay.   

Abstract

This paper presents an algorithm for automatically locating certain characteristic anatomical points called landmarks on cephalograms (skull X-rays). These landmarks are used by orthodontists in diagnosis and treatment planning. The algorithm uses digital image processing and feature recognition techniques to locate the landmarks. A resolution pyramid of the digitized cephalogram is first created. The algorithm works on the smaller, lower resolution images to locate features of interest and moves to the bigger, higher resolution images if greater location accuracy is required. Prefiltering using the median filter, contrast enhancement using histogram equalization, and edge enhancement using different gradient operators are performed on the images. The algorithm uses anatomical knowledge of the human facial structure to search for features containing the landmarks. The accuracy of the algorithm in locating the landmarks is compared with values obtained from human experts. At present the algorithm attempts to locate 10 landmarks of 27 needed for a complete analysis. All 10 landmarks have been successfully located on five cephalograms of varying quality.

Entities:  

Mesh:

Year:  1989        PMID: 2721174     DOI: 10.1016/0010-4809(89)90005-0

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


  8 in total

1.  Cephalometric image analysis and measurement for orthognathic surgery.

Authors:  J Yang; X Ling; Y Lu; M Wei; G Ding
Journal:  Med Biol Eng Comput       Date:  2001-05       Impact factor: 2.602

2.  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

3.  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

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.  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

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.  A fully deep learning model for the automatic identification of cephalometric landmarks.

Authors:  Young Hyun Kim; Chena Lee; Eun-Gyu Ha; Yoon Jeong Choi; Sang-Sun Han
Journal:  Imaging Sci Dent       Date:  2021-07-13

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
  8 in total

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