Literature DB >> 18218504

An image processing system for locating craniofacial landmarks.

J Cardillo1, M A Sid-Ahmed.   

Abstract

A new automatic target recognition algorithm has been developed to extract craniofacial landmarks from lateral skull X-rays (cephalograms). The locations of these landmarks are used by orthodontists in what is referred to as a cephalometric evaluation. The evaluation assists in the diagnosis of anomalies and in the monitoring of treatments. The algorithm is based on gray-scale mathematical morphology. A statistical approach to training was used to overcome subtle differences in skeletal topographies. Decomposition was used to desensitize the algorithm to size differences. A system was trained to locate 20 landmarks. Tests on 40 X-rays showed an 85% recognition rate on average.

Entities:  

Year:  1994        PMID: 18218504     DOI: 10.1109/42.293920

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  10 in total

1.  Diagnosis of dental deformities in cephalometry images using support vector machine.

Authors:  Arumugam Banumathi; S Raju; Varathan Abhaikumar
Journal:  J Med Syst       Date:  2009-08-11       Impact factor: 4.460

2.  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

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.  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

6.  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

7.  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.  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

9.  The application and accuracy of feature matching on automated cephalometric superimposition.

Authors:  Yiran Jiang; Guangying Song; Xiaonan Yu; Yuanbo Dou; Qingfeng Li; Siqi Liu; Bing Han; Tianmin Xu
Journal:  BMC Med Imaging       Date:  2020-03-19       Impact factor: 1.930

10.  Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

Authors:  Jeong-Hoon Lee; Hee-Jin Yu; Min-Ji Kim; Jin-Woo Kim; Jongeun Choi
Journal:  BMC Oral Health       Date:  2020-10-07       Impact factor: 2.757

  10 in total

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