curvature detection
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Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107160 ◽  
Author(s):  
Susana Novais ◽  
Susana O. Silva ◽  
Orlando Frazão

Author(s):  
Charles Tremblay-Darveau ◽  
Paul S. Sheeran ◽  
Cindy Kim Vu ◽  
Ross Williams ◽  
Matthew Bruce ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2057 ◽  
Author(s):  
Bo Zhang ◽  
Fangxin Chen ◽  
Miao Yang ◽  
Linxiang Huang ◽  
Zhijiang Du ◽  
...  

2017 ◽  
Vol 17 (10) ◽  
pp. 1384
Author(s):  
Marie Morita ◽  
Takao Sato
Keyword(s):  

2015 ◽  
Vol 14 (8) ◽  
pp. 2895-2907 ◽  
Author(s):  
Ali Mahdifar ◽  
Shahram Dehdashti ◽  
Rasoul Roknizadeh ◽  
Hongsheng Chen

2014 ◽  
Vol 19 (1) ◽  
pp. 55-66
Author(s):  
Ramūnas Markauskas ◽  
Algimantas Juozapavičius ◽  
Kęstutis Saniukas ◽  
Giedrius Bernotavičius

In this article the authors present a method for the backbone recognition and modelling. The process of recognition combines some classical techniques (Hough transformation, GVF snakes) with some new (authors present a method for initial curvature detection, which they call the Falling Ball method). The result enables us to identify high-quality features of the spine and to detect the major deformities of backbone: the intercrestal line, centre sacral vertical line, C7 plumbline; as well as angles: proximal thoracic curve, main thoracic curve, thoracolumbar/lumbar. These features are used for measure in adolescent idiopathic scoliosis, especially in the case of treatment. Input data are just radiographic images, meet in everyday practice.


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