Literature DB >> 9484208

Automatic computerized radiographic identification of cephalometric landmarks.

D J Rudolph1, P M Sinclair, J M Coggins.   

Abstract

Computerized cephalometric analysis currently requires manual identification of landmark locations. This process is time-consuming and limited in accuracy. The purpose of this study was to develop and test a novel method for automatic computer identification of cephalometric landmarks. Spatial spectroscopy (SS) is a computerized method that identifies image structure on the basis of a convolution of the image with a set of filters followed by a decision method using statistical pattern recognition techniques. By this method, characteristic features are used to recognize anatomic structures. This study compared manual identification on a computer monitor and the SS automatic method for landmark identification on minimum resolution images (0.16 cm2 per pixel). Minimum resolution (defined as the lowest resolution at which a cephalometric structure could be identified) was used to reduce computational time and memory requirements during this development stage of the SS method. Fifteen landmarks were selected on a set of 14 test images. The results showed no statistical difference (p > 0.05) in mean landmark identification errors between manual identification on the computer display and automatic identification using SS. We conclude that SS shows potential for the automatic detection of landmarks, which is an important step in the development of a completely automatic cephalometric analysis.

Mesh:

Year:  1998        PMID: 9484208     DOI: 10.1016/s0889-5406(98)70289-6

Source DB:  PubMed          Journal:  Am J Orthod Dentofacial Orthop        ISSN: 0889-5406            Impact factor:   2.650


  12 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.  On automatic landmarking.

Authors:  V Rakhshan
Journal:  Dentomaxillofac Radiol       Date:  2013-12-20       Impact factor: 2.419

3.  Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD.

Authors:  Ji-Hoon Park; Hye-Won Hwang; Jun-Ho Moon; Youngsung Yu; Hansuk Kim; Soo-Bok Her; Girish Srinivasan; Mohammed Noori A Aljanabi; Richard E Donatelli; Shin-Jae Lee
Journal:  Angle Orthod       Date:  2019-07-08       Impact factor: 2.079

4.  Assessing the Reliability of Digitalized Cephalometric Analysis in Comparison with Manual Cephalometric Analysis.

Authors:  Mohammed Umar Farooq; Mohd Asadullah Khan; Shahid Imran; Ayesha Sameera; Arshad Qureshi; Syed Afroz Ahmed; Sujan Kumar; Mohd Aziz Ur Rahman
Journal:  J Clin Diagn Res       Date:  2016-10-01

5.  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

6.  Comparative Evaluation of Conventional and OnyxCeph™ Dental Software Measurements on Cephalometric Radiography.

Authors:  Elif İzgi; Filiz Namdar Pekiner
Journal:  Turk J Orthod       Date:  2019-06-01

7.  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

8.  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

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

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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