2008
DOI: 10.1007/978-3-540-89985-3_75
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Discrimination of Bony Structures in Cephalograms for Automatic Landmark Detection

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Cited by 13 publications
(11 citation statements)
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“…Over the last three decades, several attempts had been made to create such a computerised system 4 5 6 7 8 9 10 11 . However, due to the complexity of this problem, the developed methods were unable to compete with manual landmark identification.…”
mentioning
confidence: 99%
“…Over the last three decades, several attempts had been made to create such a computerised system 4 5 6 7 8 9 10 11 . However, due to the complexity of this problem, the developed methods were unable to compete with manual landmark identification.…”
mentioning
confidence: 99%
“…In 2006, Yue et al [18] built a modified active shape model to detect 12 anatomical landmarks, achieving a 71% success rate of landmark detection within 2.0 mm and 88% within 4.0 mm. In 2009, Kafieh et al [19] combined neural networks with modified active shape models and developed a technique with 93% landmark detection accuracy for bony structures within 5.0 mm. Kaur and Singh [20] proposed an automatic cephalometric landmark detection method in 2013, which uses Zernike moment-based features for initial landmark estimation and computes small expectation window, and resulted in a mean error of 1.84 mm and standard deviation is 1.24 for 18 observed landmarks.…”
Section: Introductionmentioning
confidence: 99%
“…Using standardized cephalometric x-ray, predefined anatomic landmarks are marked so that various orthodontic and facial morphometric analyses can be applied for the diagnosis and treatment planning. Despite the several methodological limitations such as nonlinear magnification and distortion of images, its integral role in orthodontics, as well as orthognathic and facial plastic surgery is indisputable [1][2][3].…”
Section: Introductionmentioning
confidence: 99%